INTERACT Saskatoon W1 Participant Summary

The INTErventions, Research, and Action in Cities Team (INTERACT) is a national research collaboration of scientists, urban planners, and engaged citizens uncovering how the design of our cities is shaping the health and wellbeing of Canadians (www.teaminteract.ca). INTERACT is conducting longitudinal, mixed-methods natural experiment studies in four Canadian cities, with the aim of providing evidence on the impacts of urban transformations on people’s physical activity, social participation, and wellbeing, and inequalities in these outcomes.

The Saskatoon study evaluates the impacts of a Bus Rapid Transit system (BRT) along three major roadways. Participants who rode the bus at least once in a typical month were eligible to participate. Exclusion criteria across all sites were being younger than 18 years old, not being able to read or write English (or English or French in Montreal) well enough to answer an online survey and any intention to move out of the region in the next two year. Specific recruitment efforts were made to recruit participants who lived within a postal code that was within 800 m of the proposed BRT lines, but anyone in the city could participate.

Recruitment ran from September 19th to December 27th, 2018 (100 days), using social media, partner networks, in-person recruitment at bus shelters, and media appearances. Participants received a $10 gift certificate upon completion of the Health Questionnaire.

In Saskatoon, 316 participants completed the Health Questionnaire.

Section 1: Transportation

Which Saskatoon Transit Go Pass do you own?

response_labels = c(
"Monthly adult pass",
"Eco Pass",
"UPass",
"Student Pass",
"Discounted Pass",
"Low Income Pass",
"I do not use a Go pass, I use a multi-use pass",
"I do not use a Go pass, I use cash",
"other"
)


ggplot(d, aes(x = factor(d$sask_bus_pass,
                         labels = response_labels))) + geom_bar(na.rm = TRUE, fill = 1:9, alpha = 0.65) + xlab("Pass type") + theme(axis.text.x = element_text(angle = 30, hjust = 1)) 

full_table <- table_maker(d,column_name = "sask_bus_pass", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left") 

Response	N	Percentage
Monthly adult pass	60	18.99
Eco Pass	22	6.96
UPass	99	31.33
Student Pass	50	15.82
Discounted Pass	7	2.22
Low Income Pass	6	1.90
I do not use a Go pass, I use a multi-use pass	44	13.92
I do not use a Go pass, I use cash	18	5.70
other	10	3.16

On a scale of 1 to 4, with 1 being ‘very safe and 4 being ’very unsafe’, overall, how safe do you think traveling by bus is in your city?

response_labels <- c(
    "Very Safe",
    "Somewhat Safe",
    "Somewhat Unsafe",
    "Very Unsafe"
  )

ggplot(d, aes(x = factor(
  x = d$bus_safe,
  labels = response_labels
))) + geom_bar(na.rm = TRUE, fill = 1:4) + xlab("Safety") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d,column_name = "bus_safe", response_labels)

kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very Safe	137	43.35
Somewhat Safe	152	48.10
Somewhat Unsafe	21	6.65
Very Unsafe	6	1.90

On a scale of 1 to 4, with 1 being ‘very reliable’ and 4 being ‘very unreliable’, overall, how reliable do you think traveling by bus is in your city?

response_labels <- c(
    "Very reliable",
    "Somewhat reliable",
    "Somewhat unreliable",
    "Very unreliable",
    "I don't know"
  )
ggplot(d, aes(x = factor(
  x = d$bus_reliable,
  labels = response_labels
))) + geom_bar(na.rm = TRUE, fill = 1:5) + xlab("Reliability") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d,column_name = "bus_reliable", response_labels)

kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very reliable	33	10.44
Somewhat reliable	165	52.22
Somewhat unreliable	93	29.43
Very unreliable	23	7.28
I don’t know	2	0.63

On a scale of 1 to 4, with 1 being ‘very convenient’ and 4 being very inconvenient’, overall, how convenient do you think traveling by bus is in your city?

response_labels <- c(
      "Very convenient",
      "Somewhat convenient",
      "Somewhat inconvenient",
      "Very inconvenient",
      "I don't know"
      )
ggplot(d, aes(x = factor(x = d$bus_convenient, labels = response_labels))) +
      geom_bar(na.rm = TRUE, fill = 1:5) + xlab("Convenience") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "bus_reliable", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very convenient	33	10.44
Somewhat convenient	165	52.22
Somewhat inconvenient	93	29.43
Very inconvenient	23	7.28
I don’t know	2	0.63

How often do you typically travel by bus during each season?

Fall

ggplot(d, aes(x = d$bus_freq_a
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#E5364D") + xlab("Days in Fall") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(d$bus_freq_a)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   30.00   65.00   52.77   65.00   91.00

Winter

ggplot(d, aes(x = d$bus_freq_b
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#1596FF") + xlab("Days in Winter") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(d$bus_freq_b)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   39.00   65.00   55.65   65.00   91.00

Spring

ggplot(d, aes(x = d$bus_freq_c
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#76D24A") + xlab("Days in Spring") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(d$bus_freq_c)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   26.00   65.00   48.02   65.00   91.00

Summer

ggplot(d, aes(x = d$bus_freq_d
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#FFD21F") + xlab("Days in Summer") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(d$bus_freq_d)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     0.0    24.0    33.1    65.0    91.0

Do you currently travel on any of the following streets when you travel by bus in Saskatoon?

a. 8th street

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_a,
         labels = response_labels))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_a", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	182	57.59
No	127	40.19
I don’t know	7	2.22

b. 22nd street

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_b,
         labels =response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_b", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	111	35.13
No	192	60.76
I don’t know	13	4.11

c. College Drive

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_c,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_c", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	226	71.52
No	81	25.63
I don’t know	9	2.85

d. Preston Avenue

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_d,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_d", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	165	52.22
No	142	44.94
I don’t know	9	2.85

e. Attridge Drive

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_e,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_e", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	66	20.89
No	225	71.20
I don’t know	25	7.91

f. Warman Rd

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_f,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_f", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	50	15.82
No	251	79.43
I don’t know	15	4.75

g. Idylwyld Dr N

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_g,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_g", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	90	28.48
No	208	65.82
I don’t know	18	5.70

h. 3rd Avenue N

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_h,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_h", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	132	41.77
No	161	50.95
I don’t know	23	7.28

i. Broadway Avenue

response_labels <- c("Yes",
                    "No",
                    "I don't know")
ggplot(d,
       aes(x = factor(
         x = d$saskroads_i,
         labels = response_labels ))) +
      geom_bar(na.rm = TRUE, fill =3:1) + xlab("Response") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "saskroads_i", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	151	47.78
No	156	49.37
I don’t know	9	2.85

How much would you agree with the following statement: “I would like to travel by bus in Saskatoon more than I do now”?

response_labels <- c(
           "Strongly agree",
           "Somewhat agree",
           "Somewhat disagree",
           "Strongly disagree",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$sask_bus_more,
         labels = response_labels))) +
      geom_bar(na.rm = TRUE, fill =1:5)+
      xlab("agreement")+
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "sask_bus_more", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly agree	94	29.75
Somewhat agree	133	42.09
Somewhat disagree	53	16.77
Strongly disagree	25	7.91
I don’t know	11	3.48

How much more likely would you be to travel by bus more if?

a. the bus on the main part of your route ran every 10 minutes or less?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_a,
         labels = response_labels))) + 
      geom_bar(na.rm = TRUE, fill =1:4) +
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "bus_moti_a", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Much more likely	233	73.73
Somewhat more likely	60	18.99
Not at all more likely	20	6.33
I don’t know	3	0.95

b. the bus route took you closer to your destination?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_b,
         labels = response_labels))) + 
      geom_bar(na.rm = TRUE, fill =1:4) +
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "bus_moti_b", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Much more likely	184	58.23
Somewhat more likely	84	26.58
Not at all more likely	43	13.61
I don’t know	5	1.58

c. the bus and shelters were cleaner and in better condition?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_c,
         labels = response_labels))) +
      geom_bar(na.rm = TRUE, fill =1:4) + 
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "bus_moti_c", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Much more likely	135	42.72
Somewhat more likely	99	31.33
Not at all more likely	68	21.52
I don’t know	14	4.43

d. the buses were on time and transfers were more reliable?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_d,
         labels = response_labels ))) + geom_bar(na.rm = TRUE, fill =1:4) + xlab("Likelihood") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "bus_moti_d", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Much more likely	241	76.27
Somewhat more likely	59	18.67
Not at all more likely	13	4.11
I don’t know	3	0.95

e. the cost of bus passes or fare was lower?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_e,
         labels = response_labels))) +
      geom_bar(na.rm = TRUE, fill =1:4) +
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "bus_moti_e", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Much more likely	140	44.30
Somewhat more likely	88	27.85
Not at all more likely	71	22.47
I don’t know	17	5.38

Rank the following in order of how much they would influence your decision to use the bus.

a. the bus on the main part of your route ran every 10 minutes or less?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_a,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) +
      geom_bar(na.rm = TRUE , fill =1:7 , alpha= 0.65) +
      xlab("Rank")

full_table <- table_maker(d, column_name = "bus_moti_rank_a", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1	106	33.54
2	50	15.82
3	42	13.29
4	23	7.28
5	33	10.44
6	11	3.48
7	51	16.14

b. the bus route took you closer to your destination?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_b,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE , fill =1:7 , alpha= 0.65) + xlab("Rank")

full_table <- table_maker(d, column_name = "bus_moti_rank_b", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1	43	13.61
2	57	18.04
3	61	19.30
4	64	20.25
5	40	12.66
6	21	6.65
7	30	9.49

c. the bus and shelters were cleaner and in better condition?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_c,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE , fill =1:7 , alpha= 0.65) + xlab("Rank")

full_table <- table_maker(d, column_name = "bus_moti_rank_c", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1	22	6.96
2	36	11.39
3	52	16.46
4	85	26.90
5	93	29.43
6	14	4.43
7	14	4.43

d. the buses were on time and transfers were more reliable?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_d,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE , fill =1:7 , alpha= 0.65) + xlab("Rank")

full_table <- table_maker(d, column_name = "bus_moti_rank_d", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1	72	22.78
2	76	24.05
3	58	18.35
4	28	8.86
5	21	6.65
6	20	6.33
7	41	12.97

e. the cost of bus passes or fare was lower?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_e,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE , fill =1:7 , alpha= 0.65) + xlab("Rank")

full_table <- table_maker(d, column_name = "bus_moti_rank_e", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1	57	18.04
2	36	11.39
3	41	12.97
4	63	19.94
5	75	23.73
6	18	5.70
7	26	8.23

Place the slider between the following features of a future bus system, based on how important they are to you, with the slider closer to the more important feature.

ggplot(d,
       aes(x = bus_moti_slider 
       )) + geom_histogram(na.rm = TRUE, bins = 15, fill= "#76D24A")  + xlab("Rank")

summary(d$bus_moti_slider)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   10.00   23.00   32.81   49.00  100.00

Do you think Saskatoon Transit Service today is:

a. Reliable

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_a,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Reliability")

full_table <- table_maker(d, column_name = "sask_bus_now_a", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	30	9.49
Moderately	158	50.00
Slightly	85	26.90
Not at all	41	12.97
I don’t know	2	0.63

b. Clean

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_b,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("cleanness")

full_table <- table_maker(d, column_name = "sask_bus_now_b", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	51	16.14
Moderately	172	54.43
Slightly	66	20.89
Not at all	23	7.28
I don’t know	4	1.27

c. Safe

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_c,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Safety")

full_table <- table_maker(d, column_name = "sask_bus_now_c", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	100	31.65
Moderately	160	50.63
Slightly	47	14.87
Not at all	7	2.22
I don’t know	2	0.63

d. Convenient

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_d,
  labels = response_labels )
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Convenience")

full_table <- table_maker(d, column_name = "sask_bus_now_d", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	28	8.86
Moderately	113	35.76
Slightly	116	36.71
Not at all	58	18.35
I don’t know	1	0.32

e. Too expensive

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_e,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Too expensive")

full_table <- table_maker(d, column_name = "sask_bus_now_e", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	55	17.41
Moderately	84	26.58
Slightly	93	29.43
Not at all	53	16.77
I don’t know	31	9.81

f. Too cheap

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all")
x <- factor(
  x = d$sask_bus_now_f,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:4 ,
                              alpha = 0.65) + xlab("Too cheap")

full_table <- table_maker(d, column_name = "sask_bus_now_f", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	8	2.53
Moderately	21	6.65
Slightly	241	76.27
Not at all	46	14.56

g. Professional

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_g,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Professional")

full_table <- table_maker(d, column_name = "sask_bus_now_g", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	45	14.24
Moderately	134	42.41
Slightly	102	32.28
Not at all	25	7.91
I don’t know	10	3.16

h. Environmentally friendly

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_h,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Environmentally friendly")

full_table <- table_maker(d, column_name = "sask_bus_now_h", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very	45	14.24
Moderately	131	41.46
Slightly	74	23.42
Not at all	26	8.23
I don’t know	40	12.66

Have you ever heard of the Bus Rapid Transit (BRT) Corridors in Saskatoon?

response_labels <- c("Yes",
             "No")
x <- factor(
  x = d$brt_familiarity,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 3:2 ,
                              alpha = 0.65) + xlab("Response")

full_table <- table_maker(d, column_name = "brt_familiarity", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	173	54.75
No	143	45.25

Do you think that the Bus Rapid Transit (BRT) corridors are a good or bad idea for Saskatoon? It is

response_labels <-c(
    "Very good idea",
    "Somewhat good idea",
    "Somewhat bad idea",
    "Very bad idea",
    "I don't know"
  )
x <- factor(
  x = d$brt_idea,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_idea", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good idea	184	58.23
Somewhat good idea	109	34.49
Somewhat bad idea	9	2.85
Very bad idea	3	0.95
I don’t know	11	3.48

Do you think the BRT will be good for:

a. The Environment

response_labels <-  c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_a,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_a", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	98	31.01
Somewhat good	147	46.52
Somewhat bad	22	6.96
Very bad	4	1.27
I don’t know	45	14.24

b. Increasing access to transportation

response_labels <-  c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_b,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_b", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	203	64.24
Somewhat good	93	29.43
Somewhat bad	8	2.53
Very bad	3	0.95
I don’t know	9	2.85

c. Making Saskatoon a vibrant city

response_labels <-  c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_c,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_c", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	126	39.87
Somewhat good	136	43.04
Somewhat bad	8	2.53
Very bad	6	1.90
I don’t know	40	12.66

d. Tourism

response_labels <-  c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_d,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_d", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	116	36.71
Somewhat good	127	40.19
Somewhat bad	14	4.43
Very bad	5	1.58
I don’t know	54	17.09

e. Reducing traffic congestion

response_labels <-  c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_e,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_e", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	152	48.10
Somewhat good	109	34.49
Somewhat bad	21	6.65
Very bad	8	2.53
I don’t know	26	8.23

f. Health

response_labels <-  c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_f,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_f", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	80	25.32
Somewhat good	138	43.67
Somewhat bad	14	4.43
Very bad	5	1.58
I don’t know	79	25.00

g. Local business

response_labels <- c(
    "Very good",
    "Somewhat good",
    "Somewhat bad",
    "Very bad",
    "I don't know"
  )
x <- factor(
  x = d$brt_good_g,
  labels =response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "brt_good_g", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very good	101	31.96
Somewhat good	135	42.72
Somewhat bad	17	5.38
Very bad	7	2.22
I don’t know	56	17.72

Will you likely travel by bus more than you currently do once the Bus Rapid Transit (BRT) corridors are in place?

response_labels <- c("Yes",
                       "No")
x <- factor(
  x = d$brt_bus_more,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 3:2 ,
                              alpha = 0.65) + xlab("Response")

full_table <- table_maker(d, column_name = "brt_bus_more", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	254	80.38
No	62	19.62

Do you currently have a valid driver’s license?

response_labels <- c("Yes",
                       "No")
x <- factor(
  x = d$license,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 3:2 ,
                              alpha = 0.65) + xlab("Response")

full_table <- table_maker(d, column_name = "license", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	240	75.95
No	76	24.05

Do you have access to a car?

response_labels <- c("Yes",
                       "No")

x <- factor(x = d$car_access,
            labels =  response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 3:2 ,
                              alpha = 0.65) + xlab("Response")

full_table <- table_maker(d, column_name = "car_access", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	222	70.25
No	94	29.75

How do you most often access a vehicle?

Total

response_labels <- c(
    "Not applicable",
    "My household owns a vehicle",
    "I borrow a friend's or relative's vehicle",
    "I am a member of a car-sharing program (Saskatoon CarShare Co-op, etc)",
    "I access a vehicle another way (Please specify)"
  )
#Modify the data
d$cars_access_where <- d$cars_access_where %>%
      as.character() 
d$cars_access_where[which(d$cars_access_where == "[1, 2]")] <- "[1]"
x <- factor(
  x = d$cars_access_where,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:5 ,
                              alpha = 0.65) + xlab("Response") + 
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d,"cars_access_where",response_labels)

kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not applicable	94	29.75
My household owns a vehicle	196	62.03
I borrow a friend’s or relative’s vehicle	9	2.85
I am a member of a car-sharing program (Saskatoon CarShare Co-op, etc)	4	1.27
I access a vehicle another way (Please specify)	13	4.11

My household owns a vehicle

response_labels <-  c(
    "Not applicable",
    "False",
    "True"
  )
x <- factor(
  x = d$cars_access_where_1,
  labels = response_labels
)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:3 ,
                              alpha = 0.65) + xlab("Response") + 
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "cars_access_where_1", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not applicable	94	29.75
False	26	8.23
True	196	62.03

I borrow a friend’s or relative’s vehicle

response_labels <-  c(
    "Not applicable",
    "False",
    "True"
  )
x <- factor(
  x = d$cars_access_where_2,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:3 ,
                              alpha = 0.65) + xlab("Response") + 
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "cars_access_where_2", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not applicable	94	29.75
False	211	66.77
True	11	3.48

I am a member of a car-sharing program (Saskatoon CarShare Co-op, etc.)

response_labels <-  c(
    "Not applicable",
    "False",
    "True"
  )
x <- factor(
  x = d$cars_access_where_3,
  labels =  response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              fill = 1:3 ,
                              alpha = 0.65) + xlab("Response") + 
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "cars_access_where_3", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not applicable	94	29.75
False	218	68.99
True	4	1.27

How many cars, trucks, or vans are kept in your household?

response_labels <-  c("Not applicable",
             "0", "1", "2", "3", "4", "5", "6")
#set na values to 0
d$cars_household[which(is.na(d$cars_household))] <- 0
x <- factor(
  x = d$cars_household,
   labels= response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 , fill = 1:8) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "cars_household", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not applicable	120	37.97
0	4	1.27
1	82	25.95
2	69	21.84
3	22	6.96
4	15	4.75
5	3	0.95
6	1	0.32

How much do you enjoy using each transportation mode?

a. Walking

response_labels <- c("1.A lot", "2", "3", "4.Not at all ", "Not applicable")
x <- factor(
  x = d$preferred_mode_a,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 ,
                              fill = 1:5) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "preferred_mode_a", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1.A lot	139	43.99
2	86	27.22
3	69	21.84
4.Not at all	20	6.33
Not applicable	2	0.63

b. Biking

response_labels <- c("1.A lot", "2", "3", "4.Not at all ", "Not applicable")
x <- factor(
  x = d$preferred_mode_b,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 ,
                              fill = 1:5) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "preferred_mode_b", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1.A lot	89	28.16
2	73	23.10
3	45	14.24
4.Not at all	64	20.25
Not applicable	45	14.24

c. Public Transit

response_labels <- c("1.A lot", "2", "3", "4.Not at all ")
x <- factor(
  x = d$preferred_mode_c,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 ,
                              fill = 1:4) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "preferred_mode_c", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1.A lot	51	16.14
2	138	43.67
3	95	30.06
4.Not at all	32	10.13

d. Car

response_labels <- c("1.A lot", "2", "3", "4.Not at all ", "Not applicable")
x <- factor(
  x = d$preferred_mode_d,
  labels = response_labels
)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 ,
                              fill = 1:5) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "preferred_mode_d", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1.A lot	130	41.14
2	86	27.22
3	54	17.09
4.Not at all	14	4.43
Not applicable	32	10.13

e. Motorcycle or scooter

response_labels <- c("1.A lot", "2", "3", "4.Not at all ", "Not applicable")
x <- factor(
  x = d$preferred_mode_e,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 ,
                              fill = 1:5) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "preferred_mode_e", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1.A lot	5	1.58
2	13	4.11
3	16	5.06
4.Not at all	68	21.52
Not applicable	214	67.72

f. Other (Please specify)

response_labels <- c( "Not applicable","1.A lot", "2", "3", "4.Not at all ")
x <- factor(
  x = d$preferred_mode_f,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65 ,
                              fill = 1:5) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

full_table <- table_maker(d, column_name = "preferred_mode_f", response_labels)
kable(full_table) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not applicable	5	1.58
1.A lot	1	0.32
2	1	0.32
3	1	0.32
4.Not at all	308	97.47

Section 2: Physical Activity

During the last 7 days, on how many days did you do vigorous physical activities like heavy lifting, digging, heavy construction, or climbing up stairs as part of your work? Think about only those physical activities that you did for at least 10 minutes at a time.

#work_vigpa

ggplot(d, aes(x = d$work_vigpa)) + geom_histogram(na.rm = TRUE, fill = "#1596FF") + xlab("N days vigorous job-related physical activity")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$work_vigpa)), Percentage = round(as.numeric(prop.table(table(d$work_vigpa)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	194	61.39
1	16	5.06
2	26	8.23
3	17	5.38
4	24	7.59
5	22	6.96
6	6	1.90
7	11	3.48

How much time did you usually spend on one of those days doing vigorous physical activities as part of your work?

#work_vigpa_freq

d$work_vigpa_freq[d$work_vigpa_freq==-7] <- NA

ggplot(d, aes(x = d$work_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes vigorous job-related physical activity") 

summary(d$work_vigpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0    60.0   120.0   136.1   180.0   960.0     194

Again, think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days, on how many days did you do moderate physical activities like carrying light loads as part of your work? Please do not include walking.

#work_modpa

ggplot(d, aes(x = d$work_modpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days moderate job-related physical activity")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$work_modpa)), Percentage = round(as.numeric(prop.table(table(d$work_modpa)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	142	44.94
1	30	9.49
2	34	10.76
3	25	7.91
4	23	7.28
5	39	12.34
6	3	0.95
7	20	6.33

How much time did you usually spend on one of those days doing moderate physical activities as part of your work?

#work_modpa_freq

d$work_modpa_freq[d$work_modpa_freq==-7] <- NA
ggplot(d, aes(x = d$work_modpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes moderate job-related physical activity") 

summary(d$work_modpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0    30.0    60.0   121.2   180.0   480.0     142

During the last 7 days, on how many days did you travel in a motor vehicle like a train, bus, car, or metro?

#travel_motor

ggplot(d, aes(x = d$travel_motor)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$travel_motor)), Percentage = round(as.numeric(prop.table(table(d$travel_motor)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	4	1.27
1	4	1.27
2	10	3.16
3	21	6.65
4	23	7.28
5	49	15.51
6	60	18.99
7	145	45.89

How much time did you usually spend on one of those days travelling in a train, bus, car, metro, or other kind of motor vehicle?

#travel_motor_freq

d$travel_motor_freq[d$travel_motor_freq==-7] <- NA
ggplot(d, aes(x = d$travel_motor_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") 

summary(d$travel_motor_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00   30.00   60.00   77.64  120.00  900.00       4

During the last 7 days, on how many days did you bicycle for at least 10 minutes at a time to go from place to place?

#travel_bike

ggplot(d, aes(x = d$travel_bike)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$travel_bike)), Percentage = round(as.numeric(prop.table(table(d$travel_bike)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	259	81.96
1	15	4.75
2	7	2.22
3	8	2.53
4	7	2.22
5	11	3.48
6	2	0.63
7	7	2.22

How much time did you usually spend on one of those days to bicycle from place to place?

#travel_bike_freq

d$travel_bike_freq[d$travel_bike_freq==-7] <- NA
ggplot(d, aes(x = d$travel_bike_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") 

summary(d$travel_bike_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   30.00   35.00   58.07   60.00  420.00     259

During the last 7 days, on how many days did you walk for at least 10 minutes at a time to go from place to place?

#travel_walk

ggplot(d, aes(x = d$travel_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("# of days in the last 7 days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$travel_walk)), Percentage = round(as.numeric(prop.table(table(d$travel_walk)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	40	12.66
1	20	6.33
2	35	11.08
3	36	11.39
4	32	10.13
5	64	20.25
6	26	8.23
7	63	19.94

How much time did you usually spend on one of those days walking from place to place?

#travel_walk_freq

d$travel_walk_freq[d$travel_walk_freq==-7] <- NA

ggplot(d, aes(x = d$travel_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") 

summary(d$travel_walk_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00   20.00   30.00   43.85   60.00  420.00      40

Not counting any walking for transportation that you have already mentioned, during the last 7 days, on how many days did you walk for at least 10 minutes at a time in your leisure time?

#leisure_walk

ggplot(d, aes(x = d$leisure_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$leisure_walk)), Percentage = round(as.numeric(prop.table(table(d$leisure_walk))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	116	37
1	31	10
2	50	16
3	47	15
4	24	8
5	19	6
6	7	2
7	22	7

How much time did you usually spend on one of those days walking in your leisure time?

#leisure_walk_freq

d$leisure_walk_freq[d$leisure_walk_freq==-7] <- NA

ggplot(d, aes(x = d$leisure_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(d$leisure_walk_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00   20.00   30.00   56.16   60.00  900.00     116

Think about only those physical activities that you did for at least 10 minutes at a time, not counting any activity for transportation or work that you have already mentioned. During the last 7 days, on how many days did you do vigorous physical activities like aerobics, running, fast bicycling, or fast swimming in your leisure time?

#leisure_vigpa

ggplot(d, aes(x = d$leisure_vigpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$leisure_vigpa)), Percentage = round(as.numeric(prop.table(table(d$leisure_vigpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	175	55
1	31	10
2	48	15
3	24	8
4	12	4
5	15	5
6	9	3
7	2	1

How much time did you usually spend on one of those days doing vigorous physical activities in your leisure time?

#leisure_vigpa_freq

d$leisure_vigpa_freq[d$leisure_vigpa_freq==-7] <- NA

ggplot(d, aes(x = d$leisure_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(d$leisure_vigpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.00   40.00   60.00   63.89   60.00  420.00     175

During the last 7 days, on how many days did you do moderate physical activities like bicycling at a regular pace, swimming at a regular pace, or doubles tennis in your leisure time?

#leisure_modpa

ggplot(d, aes(x = d$leisure_modpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$leisure_modpa)), Percentage = round(as.numeric(prop.table(table(d$leisure_modpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	205	65
1	35	11
2	32	10
3	16	5
4	19	6
5	6	2
6	1	0
7	2	1

How much time did you usually spend on one of those days doing moderate physical activities in your leisure time?

#leisure_modpa_freq

d$leisure_modpa_freq[d$leisure_modpa_freq==-7] <- NA

ggplot(d, aes(x = d$leisure_modpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(d$leisure_modpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     2.0    30.0    45.0    56.9    60.0   480.0     205

During the last 7 days, how much time did you usually spend sitting on a weekday?

#sit_weekday

ggplot(d, aes(x = d$sit_weekday/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekday") 

summary(d$sit_weekday)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   240.0   360.0   394.1   540.0   960.0

During the last 7 days, how much time did you usually spend sitting on a weekend day?

#sit_weekend

ggplot(d, aes(x = d$sit_weekend/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekend") 

summary(d$sit_weekend)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   240.0   300.0   367.9   480.0   960.0

Section 3: General Health

How tall are you?

#height

#exclude outliers?

ggplot(d, aes(x = d$height)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#1596FF") + xlab("Height (cm)") 

summary(d$height)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   100.0   163.0   168.0   168.4   175.0   224.0

How much do you weigh?

#weight

ggplot(d, aes(x = d$weight)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#1596FF") + xlab("Weight (kg)") 

summary(d$weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   30.00   60.00   73.00   76.06   86.00  181.00

In general, would you say your health is:

#sf1

# Create proportional table
sf1 <- round(prop.table(table(d$sf1))*100,2)
sf1 <- as.data.frame(sf1)
sf1$group <- substring(rownames(sf1), 1)
# or use colnames(sf1)[1] <- "group" : 

# Change category values and transform in factor
## as.character(sf1$group) is because as.data.frame transform character into factor
sf1$group <- revalue(as.character(sf1$group), c("1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor"))

# Create plot
sf1$plot <- factor(sf1$group, sf1$group) ## Necessary to order x-axis in ggplot

sf1.plot <- ggplot(sf1, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPalette3) +
      ylab("Percent of total") +
      xlab("")
      
sf1.plot + geom_histogram(aes(x = plot), data = sf1, stat = "identity")

# make a clean summary table 
## make a dataframe on count 
sf1.tb <- as.factor(d$sf1)
sf1.tb <- summary(sf1.tb)
sf1.tb <- as.data.frame(sf1.tb)
sf1.tb$Var1 <- substring(row.names(sf1.tb), 1)
sf1.tb$group <- revalue(as.character(sf1.tb$Var1), c("1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor"))


## merge with existing prop table data used for plot above 
## order doesn't work
plot.sf1.tb <- merge(sf1, sf1.tb, by = "group")
plot.sf1.tb <- plot.sf1.tb[-c(2, 4, 6)]
plot.sf1.tb <- setcolorder(plot.sf1.tb, c("group", "sf1.tb", "Freq"))

plot.sf1.tb$order <- c(1, 4, 3, 5, 2)
plot.sf1.tb <- plot.sf1.tb %>% arrange(order)
plot.sf1.tb <- plot.sf1.tb[-c(4)]
colnames(plot.sf1.tb) <- c("Response", "N", "Percentage")

kable(plot.sf1.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left") 

Response	N	Percentage
Excellent	28	8.86
Very good	119	37.66
Good	113	35.76
Fair	48	15.19
Poor	8	2.53

The following questions are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much?

a. Moderate activities such as moving a table, pushing a vacuum cleaner, bowling, or playing golf

sf2 <- round(prop.table(table(factor(d$sf2, levels = c("1", "2", "3")), exclude = NULL))*100,2)
sf2 <- as.data.frame(sf2)
sf2$group <- substring(row.names(sf2), 1)
sf2$group <- revalue(as.character(sf2$group), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))

sf2$plot <- factor(sf2$group, sf2$group)

sf2.plot <- ggplot(sf2, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshorterfade) +
      ylab("Percent of total") +
      xlab("")
sf2.plot + geom_histogram(aes(x = plot), data = sf2, stat = "identity")

sf2.tb <- as.factor(d$sf2)
sf2.tb <- summary(sf2.tb)
sf2.tb <- as.data.frame(sf2.tb)
sf2.tb$Var1 <- substring(row.names(sf2.tb), 1)
sf2.tb$group <- revalue(as.character(sf2.tb$Var1), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))
plot.sf2.tb <- merge(sf2, sf2.tb, by = "group")
plot.sf2.tb <- plot.sf2.tb[-c(2, 4, 6)]
plot.sf2.tb <- setcolorder(plot.sf2.tb, c("group", "sf2.tb", "Freq"))
plot.sf2.tb$order <- c(3, 2, 1)
plot.sf2.tb <- plot.sf2.tb %>% arrange(order)
plot.sf2.tb <- plot.sf2.tb[-c(4)]
colnames(plot.sf2.tb) <- c("Response", "N", "Percentage")

kable(plot.sf2.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes, limited a lot	9	2.85
Yes, limited a little	47	14.87
No, not at all	260	82.28

b. Climbing several flights of stairs

# sf3 

sf3 <- round(prop.table(table(factor(d$sf3, levels = c("1", "2", "3")), exclude = NULL))*100,2)
sf3 <- as.data.frame(sf3)
sf3$group <- substring(row.names(sf3), 1)
sf3$group <- revalue(as.character(sf3$group), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))

sf3$plot <- factor(sf3$group, sf3$group)

sf3.plot <- ggplot(sf3, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshorterfade) +
      ylab("Percent of total") +
      xlab("")
      
sf3.plot + geom_histogram(aes(x = plot), data = sf3, stat = "identity") 

# summary table

sf3.tb <- as.factor(d$sf3)
sf3.tb <- summary(sf3.tb)
sf3.tb <- as.data.frame(sf3.tb)
sf3.tb$Var1 <- substring(row.names(sf3.tb), 1)
sf3.tb$group <- revalue(as.character(sf3.tb$Var1), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))
plot.sf3.tb <- merge(sf3, sf3.tb, by = "group")
plot.sf3.tb <- plot.sf3.tb[-c(2, 4, 6)]
plot.sf3.tb <- setcolorder(plot.sf3.tb, c("group", "sf3.tb", "Freq"))
plot.sf3.tb$order <- c(3, 2, 1)
plot.sf3.tb <- plot.sf3.tb %>% arrange(order)
plot.sf3.tb <- plot.sf3.tb[-c(4)]
colnames(plot.sf3.tb) <- c("Response", "N", "Percentage")

kable(plot.sf3.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes, limited a lot	18	5.70
Yes, limited a little	72	22.78
No, not at all	226	71.52

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health?

a. Accomplished less than you would like

#sf4 

sf4<- round(prop.table(table(factor(d$sf4, levels = c("1", "2")), exclude = NULL))*100,2)
sf4 <- as.data.frame(sf4)
sf4$group <- substring(row.names(sf4), 1)
sf4$group <- revalue(as.character(sf4$group), c("1" = "Yes",  "2" = "No"))

sf4$plot <- factor(sf4$group, sf4$group)

sf4.plot <- ggplot(sf4, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf4.plot + geom_histogram(aes(x = plot), data = sf4, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

sf4.tb <- as.factor(d$sf4)
sf4.tb <- summary(sf4.tb)
sf4.tb <- as.data.frame(sf4.tb)
sf4.tb$Var1 <- substring(row.names(sf4.tb), 1)
sf4.tb$group <- revalue(as.character(sf4.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf4.tb <- merge(sf4, sf4.tb, by = "group")
plot.sf4.tb <- plot.sf4.tb[-c(2, 4, 6)]
plot.sf4.tb <- setcolorder(plot.sf4.tb, c("group", "sf4.tb", "Freq"))
plot.sf4.tb$order <- c(2, 1)
plot.sf4.tb <- plot.sf4.tb %>% arrange(order)
plot.sf4.tb <- plot.sf4.tb[-c(4)]
colnames(plot.sf4.tb) <- c("Response", "N", "Percentage")

kable(plot.sf4.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	92	29.11
No	224	70.89

b. Were limited in the kind of work or other activities

#sf5
sf5<- round(prop.table(table(factor(d$sf5, levels = c("1", "2")), exclude = NULL))*100,2)
sf5 <- as.data.frame(sf5)
sf5$group <- substring(row.names(sf5), 1)
sf5$group <- revalue(as.character(sf5$group), c("1" = "Yes",  "2" = "No"))

sf5$plot <- factor(sf5$group, sf5$group)

sf5.plot <- ggplot(sf5, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf5.plot + geom_histogram(aes(x = plot), data = sf5, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

sf5.tb <- as.factor(d$sf5)
sf5.tb <- summary(sf5.tb)
sf5.tb <- as.data.frame(sf5.tb)
sf5.tb$Var1 <- substring(row.names(sf5.tb), 1)
sf5.tb$group <- revalue(as.character(sf5.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf5.tb <- merge(sf5, sf5.tb, by = "group")
plot.sf5.tb <- plot.sf5.tb[-c(2, 4, 6)]
plot.sf5.tb <- setcolorder(plot.sf5.tb, c("group", "sf5.tb", "Freq"))
plot.sf5.tb$order <- c(2, 1)
plot.sf5.tb <- plot.sf5.tb %>% arrange(order)
plot.sf5.tb <- plot.sf5.tb[-c(4)]
colnames(plot.sf5.tb) <- c("Response", "N", "Percentage")

kable(plot.sf5.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	63	19.94
No	253	80.06

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?

a. Accomplished less than you would like

#sf6

sf6<- round(prop.table(table(factor(d$sf6, levels = c("1", "2")), exclude = NULL))*100,2)
sf6 <- as.data.frame(sf6)
sf6$group <- substring(row.names(sf6), 1)
sf6$group <- revalue(as.character(sf6$group), c("1" = "Yes",  "2" = "No"))

sf6$plot <- factor(sf6$group, sf6$group)

sf6.plot <- ggplot(sf6, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf6.plot + geom_histogram(aes(x = plot), data = sf6, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

# summary table

sf6.tb <- as.factor(d$sf6)
sf6.tb <- summary(sf6.tb)
sf6.tb <- as.data.frame(sf6.tb)
sf6.tb$Var1 <- substring(row.names(sf6.tb), 1)
sf6.tb$group <- revalue(as.character(sf6.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf6.tb <- merge(sf6, sf6.tb, by = "group")
plot.sf6.tb <- plot.sf6.tb[-c(2, 4, 6)]
plot.sf6.tb <- setcolorder(plot.sf6.tb, c("group", "sf6.tb", "Freq"))
plot.sf6.tb$order <- c(2, 1)
plot.sf6.tb <- plot.sf6.tb %>% arrange(order)
plot.sf6.tb <- plot.sf6.tb[-c(4)]
colnames(plot.sf6.tb) <- c("Response", "N", "Percentage")

kable(plot.sf6.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	162	51.27
No	154	48.73

b. Did work or activities less carefully than usual

#sf7

sf7<- round(prop.table(table(factor(d$sf7, levels = c("1", "2")), exclude = NULL))*100,2)
sf7 <- as.data.frame(sf7)
sf7$group <- substring(row.names(sf7), 1)
sf7$group <- revalue(as.character(sf7$group), c("1" = "Yes",  "2" = "No"))

sf7$plot <- factor(sf7$group, sf7$group)

sf7.plot <- ggplot(sf7, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf7.plot + geom_histogram(aes(x = plot), data = sf7, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

sf7.tb <- as.factor(d$sf7)
sf7.tb <- summary(sf7.tb)
sf7.tb <- as.data.frame(sf7.tb)
sf7.tb$Var1 <- substring(row.names(sf7.tb), 1)
sf7.tb$group <- revalue(as.character(sf7.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf7.tb <- merge(sf7, sf7.tb, by = "group")
plot.sf7.tb <- plot.sf7.tb[-c(2, 4, 6)]
plot.sf7.tb <- setcolorder(plot.sf7.tb, c("group", "sf7.tb", "Freq"))
plot.sf7.tb$order <- c(2, 1)
plot.sf7.tb <- plot.sf7.tb %>% arrange(order)
plot.sf7.tb <- plot.sf7.tb[-c(4)]
colnames(plot.sf7.tb) <- c("Response", "N", "Percentage")

kable(plot.sf7.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Yes	105	33.23
No	211	66.77

During the past 4 weeks, how much did pain interfere with your normal work (including work outside the home and housework)?

#sf8

sf8 <- round(prop.table(table(factor(d$sf8, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100,2)
sf8 <- as.data.frame(sf8)
sf8$group <- substring(row.names(sf8), 1)
sf8$group <- revalue(as.character(sf8$group), c("1" = "Not at all",  "2" = "Slightly", "3" = "Moderately", "4" =  "Quite a bit", "5" = "Extremely"))

sf8$plot <- factor(sf8$group, sf8$group)

sf8.plot <- ggplot(sf8, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=rev(INTERACTshortfade)) +
      ylab("Percent of total") +
      xlab("")
      
sf8.plot + geom_histogram(aes(x = plot), data = sf8, stat = "identity") 

sf8.tb <- as.factor(d$sf8)
sf8.tb <- summary(sf8.tb)
sf8.tb <- as.data.frame(sf8.tb)
sf8.tb$Var1 <- substring(row.names(sf8.tb), 1)
sf8.tb$group <- revalue(as.character(sf8.tb$Var1), c("1" = "Not at all",  "2" = "Slightly", "3" = "Moderately", "4" =  "Quite a bit", "5" = "Extremely"))
plot.sf8 <- merge(sf8, sf8.tb, by = "group")
plot.sf8 <- plot.sf8[-c(2, 4, 6)]
plot.sf8 <- setcolorder(plot.sf8, c("group", "sf8.tb", "Freq"))
plot.sf8$order <- c(5,3,1,4,2)
plot.sf8 <- plot.sf8 %>% arrange(order)
plot.sf8 <- plot.sf8[-c(4)]
colnames(plot.sf8) <- c("Response", "N", "Percentage")

kable(plot.sf8) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Not at all	149	47.15
Slightly	97	30.70
Moderately	40	12.66
Quite a bit	22	6.96
Extremely	8	2.53

How much of the time during the past 4 weeks.

a. Have you felt calm and peaceful?

#sf9 

sf9 <- round(prop.table(table(factor(d$sf9, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf9 <- as.data.frame(sf9)
sf9$group <- substring(row.names(sf9), 1)
sf9$group <- revalue(as.character(sf9$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf9$plot <- factor(sf9$group, sf9$group)

sf9.plot <- ggplot(sf9, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
sf9.plot + geom_histogram(aes(x = plot), data = sf9, stat = "identity") 

sf9.tb <- as.factor(d$sf9)
sf9.tb <- summary(sf9.tb)
sf9.tb <- as.data.frame(sf9.tb)
sf9.tb$Var1 <- substring(row.names(sf9.tb), 1)
sf9.tb$group <- revalue(as.character(sf9.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf9 <- merge(sf9, sf9.tb, by = "group")
plot.sf9 <- plot.sf9[-c(2, 4, 6)]
plot.sf9 <- setcolorder(plot.sf9, c("group", "sf9.tb", "Freq"))
plot.sf9$order <- c(3,5,1,2,6,4)
plot.sf9 <- plot.sf9 %>% arrange(order)
plot.sf9 <- plot.sf9[-c(4)]
colnames(plot.sf9) <- c("Response", "N", "Percentage")

kable(plot.sf9) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
All of the time	11	3.48
Most of the time	86	27.22
A good bit of the time	91	28.80
Some of the time	72	22.78
A little of the time	50	15.82
None of the time	6	1.90

b. Did you have a lot of energy?

sf10 <- round(prop.table(table(factor(d$sf10, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf10 <- as.data.frame(sf10)
sf10$group <- substring(row.names(sf10), 1)
sf10$group <- revalue(as.character(sf10$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf10$plot <- factor(sf10$group, sf10$group)

sf10.plot <- ggplot(sf10, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
sf10.plot + geom_histogram(aes(x = plot), data = sf10, stat = "identity") 

sf10.tb <- as.factor(d$sf10)
sf10.tb <- summary(sf10.tb)
sf10.tb <- as.data.frame(sf10.tb)
sf10.tb$Var1 <- substring(row.names(sf10.tb), 1)
sf10.tb$group <- revalue(as.character(sf10.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf10 <- merge(sf10, sf10.tb, by = "group")
plot.sf10 <- plot.sf10[-c(2, 4, 6)]
plot.sf10 <- setcolorder(plot.sf10, c("group", "sf10.tb", "Freq"))
plot.sf10$order <- c(3,5,1,2,6,4)
plot.sf10 <- plot.sf10 %>% arrange(order)
plot.sf10 <- plot.sf10[-c(4)]
colnames(plot.sf10) <- c("Response", "N", "Percentage")

kable(plot.sf10) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
All of the time	8	2.53
Most of the time	65	20.57
A good bit of the time	107	33.86
Some of the time	80	25.32
A little of the time	45	14.24
None of the time	11	3.48

c. Have you felt downhearted and blue?

#check all of the time is 0 

#sf11

sf11 <- round(prop.table(table(factor(d$sf11, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf11 <- as.data.frame(sf11)
sf11$group <- substring(row.names(sf11), 1)
sf11$group <- revalue(as.character(sf11$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf11$plot <- factor(sf11$group, sf11$group)

sf11.plot <- ggplot(sf11, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
sf11.plot + geom_histogram(aes(x = plot), data = sf11, stat = "identity") 

sf11.tb <- as.factor(d$sf11)
sf11.tb <- summary(sf11.tb)
sf11.tb <- as.data.frame(sf11.tb)
sf11.tb$Var1 <- substring(row.names(sf11.tb), 1)


sf11.tb$group <- revalue(as.character(sf11.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf11 <- merge(sf11, sf11.tb, by = "group")
plot.sf11 <- plot.sf11[-c(2, 4, 6)]
plot.sf11 <- setcolorder(plot.sf11, c("group", "sf11.tb", "Freq"))
plot.sf11$order <- c(3,5,1,2,6,4)
plot.sf11 <- plot.sf11 %>% arrange(order)
plot.sf11 <- plot.sf11[-c(4)]
colnames(plot.sf11) <- c("Response", "N", "Percentage")


kable(plot.sf11) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
All of the time	5	1.58
Most of the time	30	9.49
A good bit of the time	51	16.14
Some of the time	76	24.05
A little of the time	112	35.44
None of the time	42	13.29

During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting friends, relatives, etc.)?

#sf12

sf12 <- round(prop.table(table(factor(d$sf12, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf12 <- as.data.frame(sf12)
sf12$group <- substring(row.names(sf12), 1)
sf12$group <- revalue(as.character(sf12$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf12$plot <- factor(sf12$group, sf12$group)

sf12.plot <- ggplot(sf12, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
sf12.plot + geom_histogram(aes(x = plot), data = sf12, stat = "identity") 

sf12.tb <- as.factor(d$sf12)
sf12.tb <- summary(sf12.tb)
sf12.tb <- as.data.frame(sf12.tb)
sf12.tb$Var1 <- substring(row.names(sf12.tb), 1)
sf12.tb$group <- revalue(as.character(sf12.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf12 <- merge(sf12, sf12.tb, by = "group")
plot.sf12 <- plot.sf12[-c(2, 4, 6)]
plot.sf12 <- setcolorder(plot.sf12, c("group", "sf12.tb", "Freq"))
plot.sf12$order <- c(3,5,1,2,6,4)
plot.sf12 <- plot.sf12 %>% arrange(order)
plot.sf12 <- plot.sf12[-c(4)]
colnames(plot.sf12) <- c("Response", "N", "Percentage")

kable(plot.sf12) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
All of the time	6	1.90
Most of the time	24	7.59
A good bit of the time	25	7.91
Some of the time	54	17.09
A little of the time	103	32.59
None of the time	104	32.91

Section 4: Well-being

Thinking about your own life and personal circumstances, how satisfied are you.

a. With your life as a whole?

#pwb_a

pwb_a <- round(prop.table(table(factor(d$pwb_a)))*100,2)

pwb_a <- as.data.frame(pwb_a)
pwb_a$group <- substring(row.names(pwb_a), 1)
pwb_a$group <- revalue(as.character(pwb_a$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

#cols <- c((brewer.pal(11,"RdYlGn")))
pwb_a$plot <- factor(pwb_a$group, pwb_a$group)
pwb_a.plot <- ggplot(pwb_a, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_a.plot + geom_histogram(aes(x = plot), data = pwb_a, stat = "identity") 

pwb_a.tb <- as.factor(d$pwb_a)
pwb_a.tb <- summary(pwb_a.tb)
pwb_a.tb <- as.data.frame(pwb_a.tb)
pwb_a.tb$Var1 <- substring(row.names(pwb_a.tb), 1)
                           
pwb_a.tb$group <- revalue(as.character(pwb_a.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_a <- merge(pwb_a, pwb_a.tb, by = "group")
plot.pwb_a <- plot.pwb_a[-c(2, 4, 6)]
plot.pwb_a <- setcolorder(plot.pwb_a, c("group", "pwb_a.tb", "Freq"))
plot.pwb_a$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_a <- plot.pwb_a %>% arrange(order)
plot.pwb_a <- plot.pwb_a[-c(4)]
colnames(plot.pwb_a) <- c("Response", "N", "Percentage")

kable(plot.pwb_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	1	0.32
1	2	0.63
2	3	0.95
3	14	4.43
4	15	4.75
5	19	6.01
6	45	14.24
7	78	24.68
8	77	24.37
9	41	12.97
10-Completely satisfied	21	6.65

b. With your standard of living?

#pwb_b

pwb_b <- round(prop.table(table(factor(d$pwb_b)))*100,2)

pwb_b <- as.data.frame(pwb_b)
pwb_b$group <- substring(row.names(pwb_b), 1)
pwb_b$group <- revalue(as.character(pwb_b$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

#cols <- c((brewer.pal(11,"RdYlGn")))
pwb_b$plot <- factor(pwb_b$group, pwb_b$group)
pwb_b.plot <- ggplot(pwb_b, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_b.plot + geom_histogram(aes(x = plot), data = pwb_b, stat = "identity") 

pwb_b.tb <- as.factor(d$pwb_b)
pwb_b.tb <- summary(pwb_b.tb)
pwb_b.tb <- as.data.frame(pwb_b.tb)
pwb_b.tb$Var1 <- substring(row.names(pwb_b.tb), 1)
                           
pwb_b.tb$group <- revalue(as.character(pwb_b.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_b <- merge(pwb_b, pwb_b.tb, by = "group")
plot.pwb_b <- plot.pwb_b[-c(2, 4, 6)]
plot.pwb_b <- setcolorder(plot.pwb_b, c("group", "pwb_b.tb", "Freq"))
plot.pwb_b$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_b <- plot.pwb_b %>% arrange(order)
plot.pwb_b <- plot.pwb_b[-c(4)]
colnames(plot.pwb_b) <- c("Response", "N", "Percentage")

kable(plot.pwb_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	4	1.27
1	3	0.95
2	6	1.90
3	12	3.80
4	13	4.11
5	23	7.28
6	39	12.34
7	62	19.62
8	69	21.84
9	37	11.71
10-Completely satisfied	48	15.19

c. With your health?

pwb_c <- round(prop.table(table(factor(d$pwb_c, levels=(1:11))))*100,2)

pwb_c <- as.data.frame(pwb_c)
pwb_c$group <- substring(row.names(pwb_c), 1)
pwb_c$group <- revalue(as.character(pwb_c$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_c$plot <- factor(pwb_c$group, pwb_c$group)
pwb_c.plot <- ggplot(pwb_c, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_c.plot + geom_histogram(aes(x = plot), data = pwb_c, stat = "identity") 

pwb_c.tb <- as.factor(d$pwb_c)
pwb_c.tb <- summary(pwb_c.tb)
pwb_c.tb <- as.data.frame(pwb_c.tb)
pwb_c.tb$Var1 <- substring(row.names(pwb_c.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$pwb_c.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("4")
nval.df <- nval.df[-c(1)]

pwb_c.tb <- rbind(pwb_c.tb, nval.df)
                    
pwb_c.tb$group <- revalue(as.character(pwb_c.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_c <- merge(pwb_c, pwb_c.tb, by = "group")
plot.pwb_c <- plot.pwb_c[-c(2, 4, 6)]
plot.pwb_c <- setcolorder(plot.pwb_c, c("group", "pwb_c.tb", "Freq"))
plot.pwb_c$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_c <- plot.pwb_c %>% arrange(order)
plot.pwb_c <- plot.pwb_c[-c(4)]
colnames(plot.pwb_c) <- c("Response", "N", "Percentage")

kable(plot.pwb_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	6	0.65
1	2	0.00
3	19	6.77
4	21	9.35
4	0	9.35
5	29	15.16
6	47	19.35
7	60	22.26
8	69	12.90
9	40	7.42
10-Completely satisfied	23	0.00

d. With what you are achieving in life?

pwb_d <- round(prop.table(table(factor(d$pwb_d)))*100,2)

pwb_d <- as.data.frame(pwb_d)
pwb_d$group <- substring(row.names(pwb_d), 1)
pwb_d$group <- revalue(as.character(pwb_d$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_d$plot <- factor(pwb_d$group, pwb_d$group)
pwb_d.plot <- ggplot(pwb_d, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_d.plot + geom_histogram(aes(x = plot), data = pwb_d, stat = "identity") 

pwb_d.tb <- as.factor(d$pwb_d)
pwb_d.tb <- summary(pwb_d.tb)
pwb_d.tb <- as.data.frame(pwb_d.tb)
pwb_d.tb$Var1 <- substring(row.names(pwb_d.tb), 1)
                           
pwb_d.tb$group <- revalue(as.character(pwb_d.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_d <- merge(pwb_d, pwb_d.tb, by = "group")
plot.pwb_d <- plot.pwb_d[-c(2, 4, 6)]
plot.pwb_d <- setcolorder(plot.pwb_d, c("group", "pwb_d.tb", "Freq"))
plot.pwb_d$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_d <- plot.pwb_d %>% arrange(order)
plot.pwb_d <- plot.pwb_d[-c(4)]
colnames(plot.pwb_d) <- c("Response", "N", "Percentage")

kable(plot.pwb_d) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	1	0.32
1	2	0.63
2	9	2.85
3	14	4.43
4	18	5.70
5	35	11.08
6	43	13.61
7	66	20.89
8	71	22.47
9	42	13.29
10-Completely satisfied	15	4.75

e. With your personal relationships?

pwb_e <- round(prop.table(table(factor(d$pwb_e)))*100,2)

pwb_e <- as.data.frame(pwb_e)
pwb_e$group <- substring(row.names(pwb_e), 1)
pwb_e$group <- revalue(as.character(pwb_e$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_e$plot <- factor(pwb_e$group, pwb_e$group)
pwb_e.plot <- ggplot(pwb_e, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_e.plot + geom_histogram(aes(x = plot), data = pwb_e, stat = "identity") 

pwb_e.tb <- as.factor(d$pwb_e)
pwb_e.tb <- summary(pwb_e.tb)
pwb_e.tb <- as.data.frame(pwb_e.tb)
pwb_e.tb$Var1 <- substring(row.names(pwb_e.tb), 1)
                           
pwb_e.tb$group <- revalue(as.character(pwb_e.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_e <- merge(pwb_e, pwb_e.tb, by = "group")
plot.pwb_e <- plot.pwb_e[-c(2, 4, 6)]
plot.pwb_e <- setcolorder(plot.pwb_e, c("group", "pwb_e.tb", "Freq"))
plot.pwb_e$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_e <- plot.pwb_e %>% arrange(order)
plot.pwb_e <- plot.pwb_e[-c(4)]
colnames(plot.pwb_e) <- c("Response", "N", "Percentage")

kable(plot.pwb_e) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	1	0.32
1	4	1.27
2	12	3.80
3	14	4.43
4	11	3.48
5	25	7.91
6	35	11.08
7	52	16.46
8	65	20.57
9	67	21.20
10-Completely satisfied	30	9.49

f. With how safe you feel?

pwb_f <- round(prop.table(table(factor(d$pwb_f)))*100,2)

pwb_f <- as.data.frame(pwb_f)
pwb_f$group <- substring(row.names(pwb_f), 1)
pwb_f$group <- revalue(as.character(pwb_f$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_f$plot <- factor(pwb_f$group, pwb_f$group)
pwb_f.plot <- ggplot(pwb_f, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_f.plot + geom_histogram(aes(x = plot), data = pwb_f, stat = "identity") 

pwb_f.tb <- as.factor(d$pwb_f)
pwb_f.tb <- summary(pwb_f.tb)
pwb_f.tb <- as.data.frame(pwb_f.tb)
pwb_f.tb$Var1 <- substring(row.names(pwb_f.tb), 1)

nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$pwb_f.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("1", "11")
nval.df <- nval.df[-c(1)]

pwb_f.tb <- rbind(pwb_f.tb, nval.df)                           
pwb_f.tb$group <- revalue(as.character(pwb_f.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))


plot.pwb_f <- merge(pwb_f, pwb_f.tb, by = "group")
plot.pwb_f <- plot.pwb_f[-c(2, 4, 6)]
plot.pwb_f <- setcolorder(plot.pwb_f, c("group", "pwb_f.tb", "Freq"))
#plot.pwb_f$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
#plot.pwb_f <- plot.pwb_f %>% arrange(order)
plot.pwb_f <- plot.pwb_f[-c(4)]
colnames(plot.pwb_f) <- c("Response", "N", "Percentage")

kable(plot.pwb_f) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	1	0.32
1	0	0.32
2	1	2.22
3	7	1.27
4	4	6.65
5	21	6.65
6	21	16.77
7	53	22.47
8	71	24.68
9	78	18.67

g. With feeling part of your community?

pwb_g <- round(prop.table(table(factor(d$pwb_g)))*100,2)

pwb_g <- as.data.frame(pwb_g)
pwb_g$group <- substring(row.names(pwb_g), 1)
pwb_g$group <- revalue(as.character(pwb_g$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_g$plot <- factor(pwb_g$group, pwb_g$group)
pwb_g.plot <- ggplot(pwb_g, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_g.plot + geom_histogram(aes(x = plot), data = pwb_g, stat = "identity") 

pwb_g.tb <- as.factor(d$pwb_g)
pwb_g.tb <- summary(pwb_g.tb)
pwb_g.tb <- as.data.frame(pwb_g.tb)
pwb_g.tb$Var1 <- substring(row.names(pwb_g.tb), 1)
                           
pwb_g.tb$group <- revalue(as.character(pwb_g.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_g <- merge(pwb_g, pwb_g.tb, by = "group")
plot.pwb_g <- plot.pwb_g[-c(2, 4, 6)]
plot.pwb_g <- setcolorder(plot.pwb_g, c("group", "pwb_g.tb", "Freq"))
plot.pwb_g$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_g <- plot.pwb_g %>% arrange(order)
plot.pwb_g <- plot.pwb_g[-c(4)]
colnames(plot.pwb_g) <- c("Response", "N", "Percentage")

kable(plot.pwb_g) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	4	1.27
1	5	1.58
2	6	1.90
3	11	3.48
4	29	9.18
5	38	12.03
6	40	12.66
7	69	21.84
8	67	21.20
9	30	9.49
10-Completely satisfied	17	5.38

h. With your future security?

pwb_h <- round(prop.table(table(factor(d$pwb_h)))*100,2)

pwb_h <- as.data.frame(pwb_h)
pwb_h$group <- substring(row.names(pwb_h), 1)
pwb_h$group <- revalue(as.character(pwb_h$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_h$plot <- factor(pwb_h$group, pwb_h$group)
pwb_h.plot <- ggplot(pwb_h, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_h.plot + geom_histogram(aes(x = plot), data = pwb_h, stat = "identity") 

pwb_h.tb <- as.factor(d$pwb_h)
pwb_h.tb <- summary(pwb_h.tb)
pwb_h.tb <- as.data.frame(pwb_h.tb)
pwb_h.tb$Var1 <- substring(row.names(pwb_h.tb), 1)
                           
pwb_h.tb$group <- revalue(as.character(pwb_h.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_h <- merge(pwb_h, pwb_h.tb, by = "group")
plot.pwb_h <- plot.pwb_h[-c(2, 4, 6)]
plot.pwb_h <- setcolorder(plot.pwb_h, c("group", "pwb_h.tb", "Freq"))
plot.pwb_h$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_h <- plot.pwb_h %>% arrange(order)
plot.pwb_h <- plot.pwb_h[-c(4)]
colnames(plot.pwb_h) <- c("Response", "N", "Percentage")

kable(plot.pwb_h) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	9	2.85
1	9	2.85
2	12	3.80
3	20	6.33
4	16	5.06
5	35	11.08
6	42	13.29
7	62	19.62
8	58	18.35
9	37	11.71
10-Completely satisfied	16	5.06

i. With your spirituality or religion?

pwb_i <- round(prop.table(table(factor(d$pwb_i)))*100,2)

pwb_i <- as.data.frame(pwb_i)
pwb_i$group <- substring(row.names(pwb_i), 1)
pwb_i$group <- revalue(as.character(pwb_i$group), c("1" = "0- Completely dissatisfied", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7", "9" = "8", "10" = "9", "11" = "10-Completely satisfied"))

pwb_i$plot <- factor(pwb_i$group, pwb_i$group)
pwb_i.plot <- ggplot(pwb_i, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")
      
pwb_i.plot + geom_histogram(aes(x = plot), data = pwb_i, stat = "identity") 

pwb_i.tb <- as.factor(d$pwb_i)
pwb_i.tb <- summary(pwb_i.tb)
pwb_i.tb <- as.data.frame(pwb_i.tb)
pwb_i.tb$Var1 <- substring(row.names(pwb_i.tb), 1)
                           
pwb_i.tb$group <- revalue(as.character(pwb_i.tb$Var1), c("0" = "0- Completely dissatisfied", "10" = "10-Completely satisfied"))

plot.pwb_i <- merge(pwb_i, pwb_i.tb, by = "group")
plot.pwb_i <- plot.pwb_i[-c(2, 4, 6)]
plot.pwb_i <- setcolorder(plot.pwb_i, c("group", "pwb_i.tb", "Freq"))
plot.pwb_i$order <- c(1, 2, 11, 3, 4, 5, 6, 7, 8, 9, 10)
plot.pwb_i <- plot.pwb_i %>% arrange(order)
plot.pwb_i <- plot.pwb_i[-c(4)]
colnames(plot.pwb_i) <- c("Response", "N", "Percentage")

kable(plot.pwb_i) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
0- Completely dissatisfied	4	1.27
1	2	0.63
2	7	2.22
3	7	2.22
4	13	4.11
5	39	12.34
6	26	8.23
7	41	12.97
8	60	18.99
9	44	13.92
10-Completely satisfied	73	23.10

In general, I consider myself:

#gwb_a

gwb_a <- round(prop.table(table(factor(d$gwb_a)))*100,2)
gwb_a <- as.data.frame(gwb_a)
gwb_a$group <- substring(row.names(gwb_a), 1)
gwb_a$group <- revalue(as.character(gwb_a$group), c("1" = "1- Not a very happy person", "7" = "7- A very happy person"))
gwb_a$plot <- factor(gwb_a$group, gwb_a$group)

p <- ggplot(gwb_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) #order responses as in gwb_a

p + geom_histogram(aes(x = plot), data = gwb_a, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

gwb_a.tb <- as.factor(d$gwb_a)
gwb_a.tb <- summary(gwb_a.tb)
gwb_a.tb <- as.data.frame(gwb_a.tb)
gwb_a.tb$Var1 <- substring(row.names(gwb_a.tb), 1)
gwb_a.tb$group <- revalue(as.character(gwb_a.tb$Var1), c("1" = "1- Not a very happy person", "7" = "7- A very happy person"))

plot.gwb_a <- merge(gwb_a, gwb_a.tb, by = "group")
plot.gwb_a <- plot.gwb_a[-c(2, 4, 6)]
plot.gwb_a <- setcolorder(plot.gwb_a, c("group", "gwb_a.tb", "Freq"))
colnames(plot.gwb_a) <- c("Response", "N", "Percentage")

kable(plot.gwb_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1- Not a very happy person	8	2.53
2	10	3.16
3	23	7.28
4	40	12.66
5	93	29.43
6	99	31.33
7- A very happy person	43	13.61

Compared with most of my peers, I consider myself:

#gwb_b

gwb_b <- round(prop.table(table(factor(d$gwb_b)))*100,2)
gwb_b <- as.data.frame(gwb_b)
gwb_b$group <- substring(row.names(gwb_b), 1)
gwb_b$group <- revalue(as.character(gwb_b$group), c("1" = "1- Less happy", "7" = "7- More happy"))
gwb_b$plot <- factor(gwb_b$group, gwb_b$group)

p <- ggplot(gwb_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = gwb_b, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") 

gwb_b.tb <- as.factor(d$gwb_b)
gwb_b.tb <- summary(gwb_b.tb)
gwb_b.tb <- as.data.frame(gwb_b.tb)
gwb_b.tb$Var1 <- substring(row.names(gwb_b.tb), 1)
gwb_b.tb$group <- revalue(as.character(gwb_b.tb$Var1), c("1" = "1- Less happy", "7" = "7- More happy"))

plot.gwb_b <- merge(gwb_b, gwb_b.tb, by = "group")
plot.gwb_b <- plot.gwb_b[-c(2, 4, 6)]
plot.gwb_b <- setcolorder(plot.gwb_b, c("group", "gwb_b.tb", "Freq"))
colnames(plot.gwb_b) <- c("Response", "N", "Percentage")

kable(plot.gwb_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1- Less happy	8	2.53
2	21	6.65
3	35	11.08
4	64	20.25
5	86	27.22
6	71	22.47
7- More happy	31	9.81

Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?

#gwb_c

gwb_c <- round(prop.table(table(factor(d$gwb_c)))*100,2)
gwb_c <- as.data.frame(gwb_c)
gwb_c$group <- substring(row.names(gwb_c), 1)
gwb_c$group <- revalue(as.character(gwb_c$group), c("1" = "1- Not at all", "7" = "7- A great deal"))
gwb_c$plot <- factor(gwb_c$group, gwb_c$group)

p <- ggplot(gwb_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = gwb_c, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") 

gwb_c.tb <- as.factor(d$gwb_c)
gwb_c.tb <- summary(gwb_c.tb)
gwb_c.tb <- as.data.frame(gwb_c.tb)
gwb_c.tb$Var1 <- substring(row.names(gwb_c.tb), 1)
gwb_c.tb$group <- revalue(as.character(gwb_c.tb$Var1), c("1" = "1- Not at all", "7" = "7- A great deal"))

plot.gwb_c <- merge(gwb_c, gwb_c.tb, by = "group")
plot.gwb_c <- plot.gwb_c[-c(2, 4, 6)]
plot.gwb_c <- setcolorder(plot.gwb_c, c("group", "gwb_c.tb", "Freq"))
colnames(plot.gwb_c) <- c("Response", "N", "Percentage")

kable(plot.gwb_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1- Not at all	13	4.11
2	24	7.59
3	38	12.03
4	45	14.24
5	89	28.16
6	71	22.47
7- A great deal	36	11.39

Some people are generally not very happy. Although they are not depressed, they never seem as happy as they might be. To what extent does this characterization describe you?

#gwb_d

gwb_d <- round(prop.table(table(factor(d$gwb_d, levels = 1:7)))*100,2)
gwb_d <- as.data.frame(gwb_d)
gwb_d$group <- substring(row.names(gwb_d), 1)
gwb_d$group <- revalue(as.character(gwb_d$group), c("1" = "1- Not at all", "7" = "7- A great deal"))
gwb_d$plot <- factor(gwb_d$group, gwb_d$group)

p <- ggplot(gwb_d, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = gwb_d, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

gwb_d.tb <- as.factor(d$gwb_d)
gwb_d.tb <- summary(gwb_d.tb)
gwb_d.tb <- as.data.frame(gwb_d.tb)
gwb_d.tb$Var1 <- substring(row.names(gwb_d.tb), 1)
gwb_d.tb$group <- revalue(as.character(gwb_d.tb$Var1), c("1" = "1- Not at all", "7" = "7- A great deal"))

plot.gwb_d <- merge(gwb_d, gwb_d.tb, by = "group")
plot.gwb_d <- plot.gwb_d[-c(2, 4, 6)]
plot.gwb_d <- setcolorder(plot.gwb_d, c("group", "gwb_d.tb", "Freq"))
colnames(plot.gwb_d) <- c("Response", "N", "Percentage")

kable(plot.gwb_d) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
1- Not at all	57	18.04
2	81	25.63
3	48	15.19
4	48	15.19
5	47	14.87
6	26	8.23
7- A great deal	9	2.85

The next questions are about how you feel about different aspects of your life. For each one, tell us how often you feel that way.

a. How often do you feel that you lack companionship?

#loneliness_a

loneliness_a <- round(prop.table(table(factor(d$loneliness_a, levels = 1:3)))*100,2)
loneliness_a <- as.data.frame(loneliness_a)
loneliness_a$group <- substring(row.names(loneliness_a), 1)
loneliness_a$group <- revalue(as.character(loneliness_a$group), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))
loneliness_a$plot <- factor(loneliness_a$group, loneliness_a$group)

p <- ggplot(loneliness_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = loneliness_a, stat = "identity") +  
  scale_fill_manual(values=rev(INTERACTshorterfade)) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("")

loneliness_a.tb <- as.factor(d$loneliness_a)
loneliness_a.tb <- summary(loneliness_a.tb)
loneliness_a.tb <- as.data.frame(loneliness_a.tb)
loneliness_a.tb$Var1 <- substring(row.names(loneliness_a.tb), 1)
loneliness_a.tb$group <- revalue(as.character(loneliness_a.tb$Var1), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))

plot.loneliness_a <- merge(loneliness_a, loneliness_a.tb, by = "group")
plot.loneliness_a <- plot.loneliness_a[-c(2, 4, 6)]
plot.loneliness_a <- setcolorder(plot.loneliness_a, c("group", "loneliness_a.tb", "Freq"))
plot.loneliness_a$order <- c(1, 3, 2)
plot.loneliness_a <- plot.loneliness_a %>% arrange(order)
plot.loneliness_a <- plot.loneliness_a[-c(4)]
colnames(plot.loneliness_a) <- c("Response", "N", "Percentage")

kable(plot.loneliness_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Hardly ever	123	38.92
Some of the time	147	46.52
Often	46	14.56

b. How often do you feel left out?

#loneliness_b

loneliness_b <- round(prop.table(table(factor(d$loneliness_b, levels = 1:3)))*100,2)
loneliness_b <- as.data.frame(loneliness_b)
loneliness_b$group <- substring(row.names(loneliness_b), 1)
loneliness_b$group <- revalue(as.character(loneliness_b$group), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))
loneliness_b$plot <- factor(loneliness_b$group, loneliness_b$group)

p <- ggplot(loneliness_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = loneliness_b, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTshorterfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

loneliness_b.tb <- as.factor(d$loneliness_b)
loneliness_b.tb <- summary(loneliness_b.tb)
loneliness_b.tb <- as.data.frame(loneliness_b.tb)
loneliness_b.tb$Var1 <- substring(row.names(loneliness_b.tb), 1)
loneliness_b.tb$group <- revalue(as.character(loneliness_b.tb$Var1), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))

plot.loneliness_b <- merge(loneliness_b, loneliness_b.tb, by = "group")
plot.loneliness_b <- plot.loneliness_b[-c(2, 4, 6)]
plot.loneliness_b <- setcolorder(plot.loneliness_b, c("group", "loneliness_b.tb", "Freq"))
plot.loneliness_b$order <- c(1, 3, 2)
plot.loneliness_b <- plot.loneliness_b %>% arrange(order)
plot.loneliness_b <- plot.loneliness_b[-c(4)]
colnames(plot.loneliness_b) <- c("Response", "N", "Percentage")

kable(plot.loneliness_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Hardly ever	128	40.51
Some of the time	142	44.94
Often	46	14.56

c. How often do you feel isolated from others?

#loneliness_c
loneliness_c <- round(prop.table(table(factor(d$loneliness_c, levels = 1:3)))*100,2)
loneliness_c <- as.data.frame(loneliness_c)
loneliness_c$group <- substring(row.names(loneliness_c), 1)
loneliness_c$group <- revalue(as.character(loneliness_c$group), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))
loneliness_c$plot <- factor(loneliness_c$group, loneliness_c$group)

p <- ggplot(loneliness_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = loneliness_c, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTshorterfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

loneliness_c.tb <- as.factor(d$loneliness_c)
loneliness_c.tb <- summary(loneliness_c.tb)
loneliness_c.tb <- as.data.frame(loneliness_c.tb)
loneliness_c.tb$Var1 <- substring(row.names(loneliness_c.tb), 1)
loneliness_c.tb$group <- revalue(as.character(loneliness_c.tb$Var1), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))

plot.loneliness_c <- merge(loneliness_c, loneliness_c.tb, by = "group")
plot.loneliness_c <- plot.loneliness_c[-c(2, 4, 6)]
plot.loneliness_c <- setcolorder(plot.loneliness_c, c("group", "loneliness_c.tb", "Freq"))
plot.loneliness_c$order <- c(1, 3, 2)
plot.loneliness_c <- plot.loneliness_c %>% arrange(order)
plot.loneliness_c <- plot.loneliness_c[-c(4)]
colnames(plot.loneliness_c) <- c("Response", "N", "Percentage")

kable(plot.loneliness_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Hardly ever	130	41.14
Some of the time	136	43.04
Often	50	15.82

Section 5: Social Participation

How would you describe your sense of belonging to your local community? Would you say it is:

#belonging

## Plot
### Data preparation
belonging <- round(prop.table(table(factor(d$belonging)))*100,2)
belonging <- as.data.frame(belonging)
belonging$group <- substring(row.names(belonging), 1)
belonging$group <- revalue(as.character(belonging$group), c("1" = "Very strong", "2" = "Somewhat strong", "3" = "Somewhat weak", "4" = "Very weak", "5" ="I don't know"))
belonging$group <- factor(belonging$group, belonging$group)

p <- ggplot(belonging, aes(x=group, y=Freq, fill=group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = group), data = belonging, stat = "identity") +
    scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("Sense of belonging")

## Table
kable(data.frame(Response = c("Very strong", "Somewhat strong", "Somewhat weak", "Very weak", "I don't know"),
           Frequence = as.numeric(table(d$belonging)), Percentage = round(as.numeric(prop.table(table(d$belonging)))*100,2))) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	Frequence	Percentage
Very strong	26	8.23
Somewhat strong	116	36.71
Somewhat weak	109	34.49
Very weak	49	15.51
I don’t know	16	5.06

How often do you.

a. Say hello to a neighbour?

#spat_a

#per week
ggplot(d, aes(x = d$spat_a/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_a/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.4603  1.9945  2.2813  3.9890  6.9808

b. Stop and have a chat with a neighbour?

#spat_b

#per week
ggplot(d, aes(x = d$spat_b/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_b/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.2301  1.0217  0.9973  6.9808

c. Visit a neighbour, or receive a visit from a neighbour?

#spat_c

#per week
ggplot(d, aes(x = d$spat_c/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_c/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.00000 0.00000 0.40183 0.08151 6.98082

d. Go somewhere (e.g., to a shop; to a restaurant), together with a neighbour?

#spat_d

#per week
ggplot(d, aes(x = d$spat_d/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_d/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.2924  0.0000  6.9808

e. Ask help/advice from or do you help/give advice to a neighbour yourself?

#spat_e

#per week
ggplot(d, aes(x = d$spat_e/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_e/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.2735  0.2301  4.9863

Thinking about your neighbourhood, how would you rate the following statements?

a. This is a close-knit neighbourhood

# plot spat2_a

spat2_a <- round(prop.table(table(factor(d$spat2_a)))*100,2)
spat2_a <- as.data.frame(spat2_a)
spat2_a$group <- substring(row.names(spat2_a), 1)
spat2_a$group <- revalue(as.character(spat2_a$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_a$plot <- factor(spat2_a$group, spat2_a$group)

p <- ggplot(spat2_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_a, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")+
      theme(plot.title = element_text(size=16))

spat2_a.tb <- as.factor(d$spat2_a)
spat2_a.tb <- summary(spat2_a.tb)
spat2_a.tb <- as.data.frame(spat2_a.tb)
spat2_a.tb$Var1 <- substring(row.names(spat2_a.tb), 1)
spat2_a.tb$group <- revalue(as.character(spat2_a.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_a.tb <- merge(spat2_a, spat2_a.tb, by = "group")
plot.spat2_a.tb <- plot.spat2_a.tb[-c(2, 4, 6)]
plot.spat2_a.tb <- setcolorder(plot.spat2_a.tb, c("group", "spat2_a.tb", "Freq"))

plot.spat2_a.tb$order <- c(3,4,2,5,1)
plot.spat2_a.tb <- plot.spat2_a.tb %>% arrange(order)
plot.spat2_a.tb <- plot.spat2_a.tb[-c(4)]
colnames(plot.spat2_a.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly disagree	64	20.25
Somewhat disagree	67	21.20
Neither agree or disagree	139	43.99
Somewhat agree	36	11.39
Strongly agree	10	3.16

b. People generally do not get along

# plot spat2_b

spat2_b <- round(prop.table(table(factor(d$spat2_b)))*100,2)
spat2_b <- as.data.frame(spat2_b)
spat2_b$group <- substring(row.names(spat2_b), 1)
spat2_b$group <- revalue(as.character(spat2_b$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_b$plot <- factor(spat2_b$group, spat2_b$group)

p <- ggplot(spat2_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_b, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_b.tb <- as.factor(d$spat2_b)
spat2_b.tb <- summary(spat2_b.tb)
spat2_b.tb <- as.data.frame(spat2_b.tb)
spat2_b.tb$Var1 <- substring(row.names(spat2_b.tb), 1)
spat2_b.tb$group <- revalue(as.character(spat2_b.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_b.tb <- merge(spat2_b, spat2_b.tb, by = "group")
plot.spat2_b.tb <- plot.spat2_b.tb[-c(2, 4, 6)]
plot.spat2_b.tb <- setcolorder(plot.spat2_b.tb, c("group", "spat2_b.tb", "Freq"))

plot.spat2_b.tb$order <- c(3,4,2,5,1)
plot.spat2_b.tb <- plot.spat2_b.tb %>% arrange(order)
plot.spat2_b.tb <- plot.spat2_b.tb[-c(4)]
colnames(plot.spat2_b.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly disagree	84	26.58
Somewhat disagree	104	32.91
Neither agree or disagree	106	33.54
Somewhat agree	18	5.70
Strongly agree	4	1.27

c. People are willing to help neighbours

# plot spat2_c
spat2_c <- round(prop.table(table(factor(d$spat2_c)))*100,2)
spat2_c <- as.data.frame(spat2_c)
spat2_c$group <- substring(row.names(spat2_c), 1)
spat2_c$group <- revalue(as.character(spat2_c$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_c$plot <- factor(spat2_c$group, spat2_c$group)

p <- ggplot(spat2_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_c, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_c.tb <- as.factor(d$spat2_c)
spat2_c.tb <- summary(spat2_c.tb)
spat2_c.tb <- as.data.frame(spat2_c.tb)
spat2_c.tb$Var1 <- substring(row.names(spat2_c.tb), 1)
spat2_c.tb$group <- revalue(as.character(spat2_c.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_c.tb <- merge(spat2_c, spat2_c.tb, by = "group")
plot.spat2_c.tb <- plot.spat2_c.tb[-c(2, 4, 6)]
plot.spat2_c.tb <- setcolorder(plot.spat2_c.tb, c("group", "spat2_c.tb", "Freq"))

plot.spat2_c.tb$order <- c(3,4,2,5,1)
plot.spat2_c.tb <- plot.spat2_c.tb %>% arrange(order)
plot.spat2_c.tb <- plot.spat2_c.tb[-c(4)]
colnames(plot.spat2_c.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_c.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly disagree	9	2.85
Somewhat disagree	24	7.59
Neither agree or disagree	107	33.86
Somewhat agree	148	46.84
Strongly agree	28	8.86

d. People do not share same values

# plot spat2_d

spat2_d <- round(prop.table(table(factor(d$spat2_d)))*100,2)
spat2_d <- as.data.frame(spat2_d)
spat2_d$group <- substring(row.names(spat2_d), 1)
spat2_d$group <- revalue(as.character(spat2_d$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_d$plot <- factor(spat2_d$group, spat2_d$group)

p <- ggplot(spat2_d, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_d, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_d.tb <- as.factor(d$spat2_d)
spat2_d.tb <- summary(spat2_d.tb)
spat2_d.tb <- as.data.frame(spat2_d.tb)
spat2_d.tb$Var1 <- substring(row.names(spat2_d.tb), 1)
spat2_d.tb$group <- revalue(as.character(spat2_d.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_d.tb <- merge(spat2_d, spat2_d.tb, by = "group")
plot.spat2_d.tb <- plot.spat2_d.tb[-c(2, 4, 6)]
plot.spat2_d.tb <- setcolorder(plot.spat2_d.tb, c("group", "spat2_d.tb", "Freq"))

plot.spat2_d.tb$order <- c(3,4,2,5,1)
plot.spat2_d.tb <- plot.spat2_d.tb %>% arrange(order)
plot.spat2_d.tb <- plot.spat2_d.tb[-c(4)]
colnames(plot.spat2_d.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_d.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly disagree	26	8.23
Somewhat disagree	70	22.15
Neither agree or disagree	180	56.96
Somewhat agree	29	9.18
Strongly agree	11	3.48

e. People can be trusted

# plot spat2_e

spat2_e <- round(prop.table(table(factor(d$spat2_e)))*100,2)
spat2_e <- as.data.frame(spat2_e)
spat2_e$group <- substring(row.names(spat2_e), 1)
spat2_e$group <- revalue(as.character(spat2_e$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_e$plot <- factor(spat2_e$group, spat2_e$group)

p <- ggplot(spat2_e, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_e, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_e.tb <- as.factor(d$spat2_e)
spat2_e.tb <- summary(spat2_e.tb)
spat2_e.tb <- as.data.frame(spat2_e.tb)
spat2_e.tb$Var1 <- substring(row.names(spat2_e.tb), 1)
spat2_e.tb$group <- revalue(as.character(spat2_e.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_e.tb <- merge(spat2_e, spat2_e.tb, by = "group")
plot.spat2_e.tb <- plot.spat2_e.tb[-c(2, 4, 6)]
plot.spat2_e.tb <- setcolorder(plot.spat2_e.tb, c("group", "spat2_e.tb", "Freq"))

plot.spat2_e.tb$order <- c(3,4,2,5,1)
plot.spat2_e.tb <- plot.spat2_e.tb %>% arrange(order)
plot.spat2_e.tb <- plot.spat2_e.tb[-c(4)]
colnames(plot.spat2_e.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_e.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly disagree	9	2.85
Somewhat disagree	34	10.76
Neither agree or disagree	96	30.38
Somewhat agree	143	45.25
Strongly agree	34	10.76

If you lost a wallet or purse that contained two hundred dollars, how likely is it to be returned with the money in it, if it was found:

a. By someone who lives close by? Would it be:

#spat3_a

spat3_a <- round(prop.table(table(factor(d$spat3_a)))*100,2)
spat3_a <- as.data.frame(spat3_a)
spat3_a$group <- substring(row.names(spat3_a), 1)
spat3_a$group <- revalue(as.character(spat3_a$group), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "4"="I don't know"))
spat3_a$plot <- factor(spat3_a$group, spat3_a$group)

p <- ggplot(spat3_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat3_a, stat = "identity") +
      scale_fill_manual(values = INTERACTPalette3) +
      guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

# make a clean summary table 
## make a dataframe on count 
spat3_a.tb <- as.factor(d$spat3_a)
spat3_a.tb <- summary(spat3_a.tb)
spat3_a.tb <- as.data.frame(spat3_a.tb)
spat3_a.tb$Var1 <- substring(row.names(spat3_a.tb), 1)
spat3_a.tb$group <- revalue(as.character(spat3_a.tb$Var1), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know"))


## merge with existing prop table data used for plot above 
plot.spat3_a.tb <- merge(spat3_a, spat3_a.tb, by = "group")
plot.spat3_a.tb <- plot.spat3_a.tb[-c(2, 4, 6)]
plot.spat3_a.tb <- setcolorder(plot.spat3_a.tb, c("group", "spat3_a.tb", "Freq"))

plot.spat3_a.tb$order <- c(4,3,2,1)
plot.spat3_a.tb <- plot.spat3_a.tb %>% arrange(order)
plot.spat3_a.tb <- plot.spat3_a.tb[-c(4)]
colnames(plot.spat3_a.tb) <- c("Response", "N", "Percentage")

kable(plot.spat3_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very likely	76	24.05
Somewhat likely	141	44.62
Not at all likely	47	14.87
I don’t know	52	16.46

b. By a complete stranger? Would it be:

#spat3_b
spat3_b <- round(prop.table(table(factor(d$spat3_b)))*100,2)
spat3_b <- as.data.frame(spat3_b)
spat3_b$group <- substring(row.names(spat3_b), 1)
spat3_b$group <- revalue(as.character(spat3_b$group), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "4"="I don't know"))
spat3_b$plot <- factor(spat3_b$group, spat3_b$group)

p <- ggplot(spat3_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat3_b, stat = "identity") +
      scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

# make a clean summary table 
## make a dataframe on count 
spat3_b.tb <- as.factor(d$spat3_b)
spat3_b.tb <- summary(spat3_b.tb)
spat3_b.tb <- as.data.frame(spat3_b.tb)
spat3_b.tb$Var1 <- substring(row.names(spat3_b.tb), 1)
spat3_b.tb$group <- revalue(as.character(spat3_b.tb$Var1), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know"))


## merge with existing prop table data used for plot above 
plot.spat3_b.tb <- merge(spat3_b, spat3_b.tb, by = "group")
plot.spat3_b.tb <- plot.spat3_b.tb[-c(2, 4, 6)]
plot.spat3_b.tb <- setcolorder(plot.spat3_b.tb, c("group", "spat3_b.tb", "Freq"))

plot.spat3_b.tb$order <- c(4,3,2,1)
plot.spat3_b.tb <- plot.spat3_b.tb %>% arrange(order)
plot.spat3_b.tb <- plot.spat3_b.tb[-c(4)]
colnames(plot.spat3_b.tb) <- c("Response", "N", "Percentage")

kable(plot.spat3_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very likely	10	3.16
Somewhat likely	92	29.11
Not at all likely	136	43.04
I don’t know	78	24.68

Here are some questions about your satisfaction with the neighbourhood in which you live. Please indicate your satisfaction with each item.How satisfied are you with…

a. your neighbourhood as a good place to live?

#neighb_a

neighb_a <- round(prop.table(table(factor(d$neighb_a)))*100,2)
neighb_a <- as.data.frame(neighb_a)
neighb_a$group <- substring(row.names(neighb_a), 1)
neighb_a$group <- revalue(as.character(neighb_a$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_a$plot <- factor(neighb_a$group, neighb_a$group)
neighb_a.plot <- ggplot(neighb_a, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3)  +
      ylab("Percent of total") +
      xlab("")
      
neighb_a.plot + geom_histogram(aes(x = plot), data = neighb_a, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_a.tb <- as.factor(d$neighb_a)
neighb_a.tb <- summary(neighb_a.tb)
neighb_a.tb <- as.data.frame(neighb_a.tb)
neighb_a.tb$Var1 <- substring(row.names(neighb_a.tb), 1)
neighb_a.tb$group <- revalue(as.character(neighb_a.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))


## merge with existing prop table data used for plot above 
plot.neighb_a.tb <- merge(neighb_a, neighb_a.tb, by = "group")
plot.neighb_a.tb <- plot.neighb_a.tb[-c(2, 4, 6)]
plot.neighb_a.tb <- setcolorder(plot.neighb_a.tb, c("group", "neighb_a.tb", "Freq"))

plot.neighb_a.tb$order <- c(4,3,2,5,1)
plot.neighb_a.tb <- plot.neighb_a.tb %>% arrange(order)
plot.neighb_a.tb <- plot.neighb_a.tb[-c(4)]
colnames(plot.neighb_a.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly satisfied	111	35.13
Satisfied	157	49.68
Neither satisfied nor dissatisfied	33	10.44
Dissatisfied	8	2.53
Strongly dissatisfied	7	2.22

b. the number of people you know in your neighbourhood?

#neighb_b

neighb_b <- round(prop.table(table(factor(d$neighb_b)))*100,2)
neighb_b <- as.data.frame(neighb_b)
neighb_b$group <- substring(row.names(neighb_b), 1)
neighb_b$group <- revalue(as.character(neighb_b$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_b$plot <- factor(neighb_b$group, neighb_b$group)
neighb_b.plot <- ggplot(neighb_b, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3)  +
      ylab("Percent of total") +
      xlab("")
      
neighb_b.plot + geom_histogram(aes(x = plot), data = neighb_b, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_b.tb <- as.factor(d$neighb_b)
neighb_b.tb <- summary(neighb_b.tb)
neighb_b.tb <- as.data.frame(neighb_b.tb)
neighb_b.tb$Var1 <- substring(row.names(neighb_b.tb), 1)
neighb_b.tb$group <- revalue(as.character(neighb_b.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))


## merge with existing prop table data used for plot above 
plot.neighb_b.tb <- merge(neighb_b, neighb_b.tb, by = "group")
plot.neighb_b.tb <- plot.neighb_b.tb[-c(2, 4, 6)]
plot.neighb_b.tb <- setcolorder(plot.neighb_b.tb, c("group", "neighb_b.tb", "Freq"))

plot.neighb_b.tb$order <- c(4,3,2,5,1)
plot.neighb_b.tb <- plot.neighb_b.tb %>% arrange(order)
plot.neighb_b.tb <- plot.neighb_b.tb[-c(4)]
colnames(plot.neighb_b.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly satisfied	31	9.81
Satisfied	96	30.38
Neither satisfied nor dissatisfied	134	42.41
Dissatisfied	45	14.24
Strongly dissatisfied	10	3.16

c. the ethnic diversity of your neighbourhood?

#neighb_c

neighb_c <- round(prop.table(table(factor(d$neighb_c)))*100,2)
neighb_c <- as.data.frame(neighb_c)
neighb_c$group <- substring(row.names(neighb_c), 1)
neighb_c$group <- revalue(as.character(neighb_c$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_c$plot <- factor(neighb_c$group, neighb_c$group)
cols <- c(rev(brewer.pal(5,"RdYlGn")),"grey")
neighb_c.plot <- ggplot(neighb_c, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3)  +
      ylab("Percent of total") +
      xlab("")
      
neighb_c.plot + geom_histogram(aes(x = plot), data = neighb_c, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_c.tb <- as.factor(d$neighb_c)
neighb_c.tb <- summary(neighb_c.tb)
neighb_c.tb <- as.data.frame(neighb_c.tb)
neighb_c.tb$Var1 <- substring(row.names(neighb_c.tb), 1)
neighb_c.tb$group <- revalue(as.character(neighb_c.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))


## merge with existing prop table data used for plot above 
plot.neighb_c.tb <- merge(neighb_c, neighb_c.tb, by = "group")
plot.neighb_c.tb <- plot.neighb_c.tb[-c(2, 4, 6)]
plot.neighb_c.tb <- setcolorder(plot.neighb_c.tb, c("group", "neighb_c.tb", "Freq"))

plot.neighb_c.tb$order <- c(4,3,2,5,1)
plot.neighb_c.tb <- plot.neighb_c.tb %>% arrange(order)
plot.neighb_c.tb <- plot.neighb_c.tb[-c(4)]
colnames(plot.neighb_c.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_c.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly satisfied	43	13.61
Satisfied	124	39.24
Neither satisfied nor dissatisfied	124	39.24
Dissatisfied	22	6.96
Strongly dissatisfied	3	0.95

d. your neighbourhood as a good place to raise children

#neighb_d

neighb_d <- round(prop.table(table(factor(d$neighb_d)))*100,2)
neighb_d <- as.data.frame(neighb_d)
neighb_d$group <- substring(row.names(neighb_d), 1)
neighb_d$group <- revalue(as.character(neighb_d$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_d$plot <- factor(neighb_d$group, neighb_d$group)

neighb_d.plot <- ggplot(neighb_d, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3)  +
      ylab("Percent of total") +
      xlab("")
      
neighb_d.plot + geom_histogram(aes(x = plot), data = neighb_d, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_d.tb <- as.factor(d$neighb_d)
neighb_d.tb <- summary(neighb_d.tb)
neighb_d.tb <- as.data.frame(neighb_d.tb)
neighb_d.tb$Var1 <- substring(row.names(neighb_d.tb), 1)
neighb_d.tb$group <- revalue(as.character(neighb_d.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))


## merge with existing prop table data used for plot above 
plot.neighb_d.tb <- merge(neighb_d, neighb_d.tb, by = "group")
plot.neighb_d.tb <- plot.neighb_d.tb[-c(2, 4, 6)]
plot.neighb_d.tb <- setcolorder(plot.neighb_d.tb, c("group", "neighb_d.tb", "Freq"))

plot.neighb_d.tb$order <- c(4,3,2,5,1)
plot.neighb_d.tb <- plot.neighb_d.tb %>% arrange(order)
plot.neighb_d.tb <- plot.neighb_d.tb[-c(4)]
colnames(plot.neighb_d.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_d.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Strongly satisfied	76	24.05
Satisfied	133	42.09
Neither satisfied nor dissatisfied	72	22.78
Dissatisfied	25	7.91
Strongly dissatisfied	10	3.16

Section 6: Neighbourhood Selection

Before moving into your current dwelling, when you were looking for a neighbourhood to live in, to what extent were the following characteristics important? Please report your perspectives, even if the neighbourhood where you currently live does not have these characteristics

a. Good access to public transportation

#neigh_pref_a

neigh_pref_a <- round(prop.table(table(factor(d$neigh_pref_a)))*100,2)
neigh_pref_a <- as.data.frame(neigh_pref_a)
neigh_pref_a$group <- substring(row.names(neigh_pref_a), 1)
neigh_pref_a$group <- revalue(as.character(neigh_pref_a$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_a$plot <- factor(neigh_pref_a$group, neigh_pref_a$group)

p <- ggplot(neigh_pref_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_a, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total")

# make a clean summary table 
## make a dataframe on count 
neigh_pref_a.tb <- as.factor(d$neigh_pref_a)
neigh_pref_a.tb <- summary(neigh_pref_a.tb)
neigh_pref_a.tb <- as.data.frame(neigh_pref_a.tb)
neigh_pref_a.tb$Var1 <- substring(row.names(neigh_pref_a.tb), 1)
neigh_pref_a.tb$group <- revalue(as.character(neigh_pref_a.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_a.tb <- merge(neigh_pref_a, neigh_pref_a.tb, by = "group")
plot.neigh_pref_a.tb <- plot.neigh_pref_a.tb[-c(2, 4, 6)]
plot.neigh_pref_a.tb <- setcolorder(plot.neigh_pref_a.tb, c("group", "neigh_pref_a.tb", "Freq"))

plot.neigh_pref_a.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_a.tb <- plot.neigh_pref_a.tb %>% arrange(order)
plot.neigh_pref_a.tb <- plot.neigh_pref_a.tb[-c(4)]
colnames(plot.neigh_pref_a.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	198	62.66
Somewhat important	65	20.57
Not very important	31	9.81
Not important at all	10	3.16
I don’t know	12	3.80

b. Sufficient parks and green spaces

#neigh_pref_b

neigh_pref_b <- round(prop.table(table(factor(d$neigh_pref_b)))*100,2)
neigh_pref_b <- as.data.frame(neigh_pref_b)
neigh_pref_b$group <- substring(row.names(neigh_pref_b), 1)
neigh_pref_b$group <- revalue(as.character(neigh_pref_b$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_b$plot <- factor(neigh_pref_b$group, neigh_pref_b$group)

p <- ggplot(neigh_pref_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_b, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("")

# make a clean summary table 
## make a dataframe on count 
neigh_pref_b.tb <- as.factor(d$neigh_pref_b)
neigh_pref_b.tb <- summary(neigh_pref_b.tb)
neigh_pref_b.tb <- as.data.frame(neigh_pref_b.tb)
neigh_pref_b.tb$Var1 <- substring(row.names(neigh_pref_b.tb), 1)
neigh_pref_b.tb$group <- revalue(as.character(neigh_pref_b.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_b.tb <- merge(neigh_pref_b, neigh_pref_b.tb, by = "group")
plot.neigh_pref_b.tb <- plot.neigh_pref_b.tb[-c(2, 4, 6)]
plot.neigh_pref_b.tb <- setcolorder(plot.neigh_pref_b.tb, c("group", "neigh_pref_b.tb", "Freq"))

plot.neigh_pref_b.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_b.tb <- plot.neigh_pref_b.tb %>% arrange(order)
plot.neigh_pref_b.tb <- plot.neigh_pref_b.tb[-c(4)]
colnames(plot.neigh_pref_b.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	118	37.34
Somewhat important	127	40.19
Not very important	49	15.51
Not important at all	10	3.16
I don’t know	12	3.80

c. Sufficient shops and services

#neigh_pref_c

neigh_pref_c <- round(prop.table(table(factor(d$neigh_pref_c)))*100,2)
neigh_pref_c <- as.data.frame(neigh_pref_c)
neigh_pref_c$group <- substring(row.names(neigh_pref_c), 1)
neigh_pref_c$group <- revalue(as.character(neigh_pref_c$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_c$plot <- factor(neigh_pref_c$group, neigh_pref_c$group)

p <- ggplot(neigh_pref_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_c, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_c.tb <- as.factor(d$neigh_pref_c)
neigh_pref_c.tb <- summary(neigh_pref_c.tb)
neigh_pref_c.tb <- as.data.frame(neigh_pref_c.tb)
neigh_pref_c.tb$Var1 <- substring(row.names(neigh_pref_c.tb), 1)
neigh_pref_c.tb$group <- revalue(as.character(neigh_pref_c.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_c.tb <- merge(neigh_pref_c, neigh_pref_c.tb, by = "group")
plot.neigh_pref_c.tb <- plot.neigh_pref_c.tb[-c(2, 4, 6)]
plot.neigh_pref_c.tb <- setcolorder(plot.neigh_pref_c.tb, c("group", "neigh_pref_c.tb", "Freq"))

plot.neigh_pref_c.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_c.tb <- plot.neigh_pref_c.tb %>% arrange(order)
plot.neigh_pref_c.tb <- plot.neigh_pref_c.tb[-c(4)]
colnames(plot.neigh_pref_c.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_c.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	122	38.61
Somewhat important	131	41.46
Not very important	42	13.29
Not important at all	10	3.16
I don’t know	11	3.48

d. Proximity to doctors, a pharmacy or other health services

#neigh_pref_d

neigh_pref_d <- round(prop.table(table(factor(d$neigh_pref_d)))*100,2)
neigh_pref_d <- as.data.frame(neigh_pref_d)
neigh_pref_d$group <- substring(row.names(neigh_pref_d), 1)
neigh_pref_d$group <- revalue(as.character(neigh_pref_d$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_d$plot <- factor(neigh_pref_d$group, neigh_pref_d$group)

p <- ggplot(neigh_pref_d, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_d, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_d.tb <- as.factor(d$neigh_pref_d)
neigh_pref_d.tb <- summary(neigh_pref_d.tb)
neigh_pref_d.tb <- as.data.frame(neigh_pref_d.tb)
neigh_pref_d.tb$Var1 <- substring(row.names(neigh_pref_d.tb), 1)
neigh_pref_d.tb$group <- revalue(as.character(neigh_pref_d.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_d.tb <- merge(neigh_pref_d, neigh_pref_d.tb, by = "group")
plot.neigh_pref_d.tb <- plot.neigh_pref_d.tb[-c(2, 4, 6)]
plot.neigh_pref_d.tb <- setcolorder(plot.neigh_pref_d.tb, c("group", "neigh_pref_d.tb", "Freq"))

plot.neigh_pref_d.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_d.tb <- plot.neigh_pref_d.tb %>% arrange(order)
plot.neigh_pref_d.tb <- plot.neigh_pref_d.tb[-c(4)]
colnames(plot.neigh_pref_d.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_d.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	88	27.85
Somewhat important	106	33.54
Not very important	73	23.10
Not important at all	35	11.08
I don’t know	14	4.43

e. A good knowledge of the neighbourhood

#neigh_pref_e

neigh_pref_e <- round(prop.table(table(factor(d$neigh_pref_e)))*100,2)
neigh_pref_e <- as.data.frame(neigh_pref_e)
neigh_pref_e$group <- substring(row.names(neigh_pref_e), 1)
neigh_pref_e$group <- revalue(as.character(neigh_pref_e$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_e$plot <- factor(neigh_pref_e$group, neigh_pref_e$group)

p <- ggplot(neigh_pref_e, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_e, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("")

# make a clean summary table 
## make a dataframe on count 
neigh_pref_e.tb <- as.factor(d$neigh_pref_e)
neigh_pref_e.tb <- summary(neigh_pref_e.tb)
neigh_pref_e.tb <- as.data.frame(neigh_pref_e.tb)
neigh_pref_e.tb$Var1 <- substring(row.names(neigh_pref_e.tb), 1)
neigh_pref_e.tb$group <- revalue(as.character(neigh_pref_e.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_e.tb <- merge(neigh_pref_e, neigh_pref_e.tb, by = "group")
plot.neigh_pref_e.tb <- plot.neigh_pref_e.tb[-c(2, 4, 6)]
plot.neigh_pref_e.tb <- setcolorder(plot.neigh_pref_e.tb, c("group", "neigh_pref_e.tb", "Freq"))

plot.neigh_pref_e.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_e.tb <- plot.neigh_pref_e.tb %>% arrange(order)
plot.neigh_pref_e.tb <- plot.neigh_pref_e.tb[-c(4)]
colnames(plot.neigh_pref_e.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_e.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	68	21.52
Somewhat important	122	38.61
Not very important	89	28.16
Not important at all	23	7.28
I don’t know	14	4.43

f. Presence of relatives, friends or acquaintances

#neigh_pref_f

neigh_pref_f <- round(prop.table(table(factor(d$neigh_pref_f)))*100,2)
neigh_pref_f <- as.data.frame(neigh_pref_f)
neigh_pref_f$group <- substring(row.names(neigh_pref_f), 1)
neigh_pref_f$group <- revalue(as.character(neigh_pref_f$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_f$plot <- factor(neigh_pref_f$group, neigh_pref_f$group)

p <- ggplot(neigh_pref_f, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_f, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_f.tb <- as.factor(d$neigh_pref_f)
neigh_pref_f.tb <- summary(neigh_pref_f.tb)
neigh_pref_f.tb <- as.data.frame(neigh_pref_f.tb)
neigh_pref_f.tb$Var1 <- substring(row.names(neigh_pref_f.tb), 1)
neigh_pref_f.tb$group <- revalue(as.character(neigh_pref_f.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_f.tb <- merge(neigh_pref_f, neigh_pref_f.tb, by = "group")
plot.neigh_pref_f.tb <- plot.neigh_pref_f.tb[-c(2, 4, 6)]
plot.neigh_pref_f.tb <- setcolorder(plot.neigh_pref_f.tb, c("group", "neigh_pref_f.tb", "Freq"))

plot.neigh_pref_f.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_f.tb <- plot.neigh_pref_f.tb %>% arrange(order)
plot.neigh_pref_f.tb <- plot.neigh_pref_f.tb[-c(4)]
colnames(plot.neigh_pref_f.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_f.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	51	16.14
Somewhat important	79	25.00
Not very important	91	28.80
Not important at all	71	22.47
I don’t know	24	7.59

g. A neighbourhood where it is pleasant to walk

#neigh_pref_g

neigh_pref_g <- round(prop.table(table(factor(d$neigh_pref_g)))*100,2)
neigh_pref_g <- as.data.frame(neigh_pref_g)
neigh_pref_g$group <- substring(row.names(neigh_pref_g), 1)
neigh_pref_g$group <- revalue(as.character(neigh_pref_g$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_g$plot <- factor(neigh_pref_g$group, neigh_pref_g$group)

p <- ggplot(neigh_pref_g, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_g, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_g.tb <- as.factor(d$neigh_pref_g)
neigh_pref_g.tb <- summary(neigh_pref_g.tb)
neigh_pref_g.tb <- as.data.frame(neigh_pref_g.tb)
neigh_pref_g.tb$Var1 <- substring(row.names(neigh_pref_g.tb), 1)
neigh_pref_g.tb$group <- revalue(as.character(neigh_pref_g.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_g.tb <- merge(neigh_pref_g, neigh_pref_g.tb, by = "group")
plot.neigh_pref_g.tb <- plot.neigh_pref_g.tb[-c(2, 4, 6)]
plot.neigh_pref_g.tb <- setcolorder(plot.neigh_pref_g.tb, c("group", "neigh_pref_g.tb", "Freq"))

plot.neigh_pref_g.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_g.tb <- plot.neigh_pref_g.tb %>% arrange(order)
plot.neigh_pref_g.tb <- plot.neigh_pref_g.tb[-c(4)]
colnames(plot.neigh_pref_g.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_g.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	129	40.82
Somewhat important	120	37.97
Not very important	39	12.34
Not important at all	18	5.70
I don’t know	10	3.16

h. A neighbourhood where it is practical to move around by car (ease of parking, low traffic, good access by car)

#neigh_pref_h

neigh_pref_h <- round(prop.table(table(factor(d$neigh_pref_h)))*100,2)
neigh_pref_h <- as.data.frame(neigh_pref_h)
neigh_pref_h$group <- substring(row.names(neigh_pref_h), 1)
neigh_pref_h$group <- revalue(as.character(neigh_pref_h$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_h$plot <- factor(neigh_pref_h$group, neigh_pref_h$group)

p <- ggplot(neigh_pref_h, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_h, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_h.tb <- as.factor(d$neigh_pref_h)
neigh_pref_h.tb <- summary(neigh_pref_h.tb)
neigh_pref_h.tb <- as.data.frame(neigh_pref_h.tb)
neigh_pref_h.tb$Var1 <- substring(row.names(neigh_pref_h.tb), 1)
neigh_pref_h.tb$group <- revalue(as.character(neigh_pref_h.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_h.tb <- merge(neigh_pref_h, neigh_pref_h.tb, by = "group")
plot.neigh_pref_h.tb <- plot.neigh_pref_h.tb[-c(2, 4, 6)]
plot.neigh_pref_h.tb <- setcolorder(plot.neigh_pref_h.tb, c("group", "neigh_pref_h.tb", "Freq"))

plot.neigh_pref_h.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_h.tb <- plot.neigh_pref_h.tb %>% arrange(order)
plot.neigh_pref_h.tb <- plot.neigh_pref_h.tb[-c(4)]
colnames(plot.neigh_pref_h.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_h.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	86	27.22
Somewhat important	105	33.23
Not very important	55	17.41
Not important at all	49	15.51
I don’t know	21	6.65

i. Presence of good schools

#neigh_pref_i

neigh_pref_i <- round(prop.table(table(factor(d$neigh_pref_i)))*100,2)
neigh_pref_i <- as.data.frame(neigh_pref_i)
neigh_pref_i$group <- substring(row.names(neigh_pref_i), 1)
neigh_pref_i$group <- revalue(as.character(neigh_pref_i$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_i$plot <- factor(neigh_pref_i$group, neigh_pref_i$group)

p <- ggplot(neigh_pref_i, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_i, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_i.tb <- as.factor(d$neigh_pref_i)
neigh_pref_i.tb <- summary(neigh_pref_i.tb)
neigh_pref_i.tb <- as.data.frame(neigh_pref_i.tb)
neigh_pref_i.tb$Var1 <- substring(row.names(neigh_pref_i.tb), 1)
neigh_pref_i.tb$group <- revalue(as.character(neigh_pref_i.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_i.tb <- merge(neigh_pref_i, neigh_pref_i.tb, by = "group")
plot.neigh_pref_i.tb <- plot.neigh_pref_i.tb[-c(2, 4, 6)]
plot.neigh_pref_i.tb <- setcolorder(plot.neigh_pref_i.tb, c("group", "neigh_pref_i.tb", "Freq"))

plot.neigh_pref_i.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_i.tb <- plot.neigh_pref_i.tb %>% arrange(order)
plot.neigh_pref_i.tb <- plot.neigh_pref_i.tb[-c(4)]
colnames(plot.neigh_pref_i.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_i.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Very important	81	25.63
Somewhat important	66	20.89
Not very important	53	16.77
Not important at all	86	27.22
I don’t know	30	9.49

Section 7: Demographics

Thinking about where you live now, are you

#house_tenure

house_tenure <- round(prop.table(table(factor(d$house_tenure, levels = c(1:6))))*100,2)
house_tenure <- as.data.frame(house_tenure)
house_tenure$group <- substring(row.names(house_tenure), 1)
house_tenure$group <- revalue(as.character(house_tenure$group), c("1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "6" = "I don't know"))

house_tenure$plot <- factor(house_tenure$group, house_tenure$group)

house_tenure.plot <- ggplot(house_tenure, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteSet) +
      ylab("Percent of total") +
      xlab("")
      
house_tenure.plot + geom_histogram(aes(x = plot), data = house_tenure, stat = "identity") 

house_tenure.tb <- as.factor(d$house_tenure)
house_tenure.tb <- summary(house_tenure.tb)
house_tenure.tb <- as.data.frame(house_tenure.tb)
house_tenure.tb$Var1 <- substring(row.names(house_tenure.tb), 1)


house_tenure.tb$group <- revalue(as.character(house_tenure.tb$Var1), c("1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "77" = "I don't know"))

## merge with existing prop table data used for plot above 
plot.house_tenure.tb <- merge(house_tenure, house_tenure.tb, by = "group")
plot.house_tenure.tb <- plot.house_tenure.tb[-c(2, 4, 6)]
plot.house_tenure.tb <- setcolorder(plot.house_tenure.tb, c("group", "house_tenure.tb", "Freq"))

plot.house_tenure.tb$order <- c(2,1,6,5,3,4)
plot.house_tenure.tb <- plot.house_tenure.tb %>% arrange(order)
plot.house_tenure.tb <- plot.house_tenure.tb[-c(4)]
colnames(plot.house_tenure.tb) <- c("Response", "N", "Percentage")

kable(plot.house_tenure.tb) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")   

Response	N	Percentage
An owner	109	34.71
A tenant	145	46.18
Resident in a relative or friend’s home	47	14.97
Resident other than in a relative or friend’s home	6	1.91
Other	7	2.23
I don’t know	2	0.00

In what type of dwelling do you currently live? Is it

#dwelling_type

dwelling_type <-round(prop.table(table(factor(d$dwelling_type, levels=c("1","2", "3", "4", "5", "6", "7", "8","9","77"))))*100,2)
dwelling_type <- as.data.frame(dwelling_type)
dwelling_type$answer <- substring(row.names(dwelling_type), 1)
dwelling_type$answer <- revalue(as.character(dwelling_type$answer), c("1" = "Single detached house", "2" = "Semi-detached house", "3" = "Row house",  "4" = "An apartment (or condo) in a duplex or triplex", "5" = "Apartment (or condo) in building with fewer than 5 storeys", "6" = "Apartment (or condo) in building with more than 5 storeys", "7" = "Mobile home/movable dwelling", "8" = "Senior's home", "9" = "Other", "10" = "Don't know/prefer not to say"))

dwelling_type$plot <- factor(dwelling_type$answer, dwelling_type$answer)

dwelling_type.plot <- ggplot(dwelling_type, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
  guides(fill = FALSE) +
  scale_fill_manual(values= INTERACTPaletteSet) +
  ylab("Percent of total") +
  xlab("")

dwelling_type.plot + geom_histogram(aes(x = plot), data = dwelling_type, stat = "identity") 

dwelling_type.tb <- as.factor(d$dwelling_type)
dwelling_type.tb <- summary(dwelling_type.tb)
dwelling_type.tb <- as.data.frame(dwelling_type.tb)
dwelling_type.tb$Var1 <- substring(row.names(dwelling_type.tb), 1)


dwelling_type.tb$answer <- revalue(as.character(dwelling_type.tb$Var1), c("1" = "Single detached house", "2" = "Semi-detached house", "3" = "Row house",  "4" = "An apartment (or condo) in a duplex or triplex", "5" = "Apartment (or condo) in building with fewer than 5 storeys", "6" = "Apartment (or condo) in building with more than 5 storeys", "7" = "Mobile home/movable dwelling", "8" = "Senior's home", "9" = "Other", "77" = "Don't know/prefer not to say"))

## merge with existing prop table data used for plot above 
plot.dwelling_type.tb <- merge(dwelling_type, dwelling_type.tb, by = "answer")
plot.dwelling_type.tb <- plot.dwelling_type.tb %>% arrange(Var1.x)
plot.dwelling_type.tb <- plot.dwelling_type.tb[-c(2, 4, 6)]
plot.dwelling_type.tb <- setcolorder(plot.dwelling_type.tb, c("answer", "dwelling_type.tb", "Freq"))

colnames(plot.dwelling_type.tb) <- c("Response", "N", "Percentage")

kable(plot.dwelling_type.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Single detached house	153	48.42
Semi-detached house	18	5.70
Row house	20	6.33
An apartment (or condo) in a duplex or triplex	14	4.43
Apartment (or condo) in building with fewer than 5 storeys	66	20.89
Apartment (or condo) in building with more than 5 storeys	23	7.28
Mobile home/movable dwelling	1	0.32
Senior’s home	1	0.32
Other	14	4.43
Don’t know/prefer not to say	6	1.90

When did you move to your current residence?

#residence

residence <- as.integer(format(as.Date(d$residence),"%Y"))
time <- 2019 - residence

ggplot(d, aes(x = time)) + geom_histogram(na.rm=TRUE, binwidth = 1, fill="#1596FF") + xlab("Years since moving to current residence") 

summary(time)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   3.000   6.041   7.000  42.000

What is your current gender identity?

#gender 

gender <- round(prop.table(table(factor(d$gender, levels = c("1", "2", "3", "4", "5", "6"))))*100,2)
gender <- as.data.frame(gender)
gender$response <- substring(row.names(gender), 1)
gender$response <- revalue(as.factor(gender$response), c("1"="Man","2"="Woman","3"="Trans man", "4"="Trans woman", "5"="Genderqueer/Gender non-conforming", "6"="Different identity"))

gender$response <- factor(gender$response, gender$response)

p <- ggplot(gender, aes(x = response, y = Freq, fill = response)) + theme(axis.text.x = element_text(size=12, angle=90, vjust = .6, hjust= 1))

p + geom_histogram(aes(x = response), data = gender, stat = "identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("Gender") 

#table 
gender.tb <- as.factor(d$gender)
gender.tb <- summary(gender.tb)
gender.tb <- as.data.frame(gender.tb)
gender.tb$Var1 <- substring(row.names(gender.tb), 1)

#insert missing values
nval.df <- c("0", "0") 
nval.df <- as.data.frame(nval.df)
nval.df$gender.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("4", "6")
nval.df <- nval.df[-c(1)]

gender.tb <- rbind(gender.tb, nval.df)

gender.tb$response <- revalue(as.character(gender.tb$Var1), c("1"="Man","2"="Woman","3"="Trans man", "4"="Trans woman", "5"="Genderqueer/Gender non-conforming", "6"="Different identity"))

plot.gender <- merge(gender, gender.tb, by = "response")

plot.gender <- plot.gender[-c(2, 5)]
plot.gender <- setcolorder(plot.gender, c("response", "gender.tb", "Freq"))
plot.gender$order <- c(6,5,1,3,4,2)
plot.gender <- plot.gender %>% arrange(order)
plot.gender <- plot.gender[-c(4)]
colnames(plot.gender) <- c("Response", "N", "Percentage")

kable(plot.gender) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Man	80	25.32
Woman	233	73.73
Trans man	1	0.32
Trans woman	0	0.00
Genderqueer/Gender non-conforming	2	0.63
Different identity	0	0.00

What sex were you assigned at birth?

# Sex

sex <- prop.table(table(factor(Health$sex, levels = 1:3)))*100
sex <- as.data.frame(sex)
sex$response <- substring(row.names(sex), 1)
sex$response <- revalue(as.factor(sex$response), c("1" = "Male", "2" = "Female", "3" = "Other"))

sex$response <- factor(sex$response, sex$response)

p <- ggplot(sex, aes(x = response, y = Freq, fill = response)) + theme(axis.text.x = element_text(angle=90, vjust = .6))

p + geom_histogram(aes(x = response), data = sex, stat = "identity") +
 scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("Sex") 

## Table- 
sex.tb <- as.factor(d$sex)
sex.tb <- summary(sex.tb)
sex.tb <- as.data.frame(sex.tb)
sex.tb$Var1 <- substring(row.names(sex.tb), 1)

#insert missing values
nval.df <- c("0") 
nval.df <- as.data.frame(nval.df)
nval.df$sex.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("3")
nval.df <- nval.df[-c(1)]

sex.tb <- rbind(sex.tb, nval.df)

sex.tb$response <- revalue(as.character(sex.tb$Var1), c("1" = "Male", "2" = "Female", "3" = "Other"))

plot.sex <- merge(sex, sex.tb, by = "response")

plot.sex <- plot.sex[-c(2, 5)]
plot.sex <- setcolorder(plot.sex, c("response", "sex.tb", "Freq"))
plot.sex$order <- c(2,1,3)
plot.sex <- plot.sex %>% arrange(order)
plot.sex <- plot.sex[-c(4)]
colnames(plot.sex) <- c("Response", "N", "Percentage")

kable(plot.sex) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")  

Response	N	Percentage
Male	80	25.31646
Female	236	74.68354
Other	0	0.00000

What is your marital status? Are you…

#marital_status 

marital <- prop.table(table(factor(d$marital_status, levels = c("1", "2", "3", "4"))))*100
marital <- as.data.frame(marital)
marital$group <- substring(row.names(marital), 1)
marital$group <- revalue(as.character(marital$group), c("1" = "Single", "2" = "Married/commonlaw", "3" = "Separated/divorced", "4" = "Widowed"))

marital$plot <- factor(marital$group, marital$group)

marital.plot <- ggplot(marital, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteSet)  +
      ylab("Percent of total") +
      xlab("")
      
marital.plot + geom_histogram(aes(x = plot), data = marital, stat = "identity") 

## Table- 
kable(data.frame(Response = c("Single", "Married/commonlaw", "Separated/divorced", "Widowed"),
           Frequence = as.numeric(table(d$marital_status)), Percentage = round(as.numeric(prop.table(table(d$marital_status)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")   

Response	Frequence	Percentage
Single	181	57.28
Married/commonlaw	113	35.76
Separated/divorced	19	6.01
Widowed	3	0.95

Do you have children?

#children

children <- prop.table(table(factor(d$children, levels = c("1", "2"))))*100
children <- as.data.frame(children)
children$group <- substring(row.names(children), 1)
children$group <- revalue(as.character(children$group), c("1" = "Yes",  "2" = "No"))

children$plot <- factor(children$group, children$group)

children.plot <- ggplot(children, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) #order responses as in t5

children.plot + geom_histogram(aes(x = plot), data = children, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

## Table- 
kable(data.frame(Response = c("Yes", "No"),
           Frequence = as.numeric(table(d$children)), Percentage = round(as.numeric(prop.table(table(d$children)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")   

Response	Frequence	Percentage
Yes	83	26.27
No	233	73.73

How many children do you have?

#living_children

d$living_children[d$living_children==-7] <- NA

living_children <- round(prop.table(table(factor(d$living_children)))*100,2)
living_children <- as.data.frame(living_children)
living_children <- as.data.frame(living_children)
living_children$answer <- substring(row.names(living_children), 1)
living_children$answer <- revalue(as.character(living_children$answer))

living_children$plot <- factor(living_children$answer, living_children$answer)

living_children.plot <- ggplot(living_children, aes(x = answer, y = Freq, fill = plot, na.rm = TRUE)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

living_children.plot + geom_histogram(aes(x = plot), data = living_children, stat = "identity") +
guides(fill = FALSE) +
  scale_fill_manual(values = INTERACTPalette3)+
  ylab("Percent of total") +
  xlab("Response")

living_children.tb <- data.frame(Response = c("1", "2", "3", "4", "5", "6"),
           Frequence = as.numeric(table(d$living_children)), Percentage = round(as.numeric(prop.table(table(d$living_children)))*100,2)) 


kable(living_children.tb) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")    

Response	Frequence	Percentage
1	30	36.14
2	30	36.14
3	13	15.66
4	8	9.64
5	1	1.20
6	1	1.20

What is your current living arrangement? Do you live

alone

#d$living_arrange_1[d$living_arrange_1==-7] <- NA

living_arrange_1 <- round(prop.table(table(factor(d$living_arrange_1)))*100,2)
living_arrange_1 <- as.data.frame(living_arrange_1)
living_arrange_1$group <- substring(row.names(living_arrange_1), 1)
living_arrange_1$group <- revalue(as.character(living_arrange_1$group), c("1" = "With other people",  "2" = "Alone"))

living_arrange_1$plot <- factor(living_arrange_1$group, living_arrange_1$group)

living_arrange_1.plot <- ggplot(living_arrange_1, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

living_arrange_1.plot + geom_histogram(aes(x = group), data = living_arrange_1, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

## Table- 
kable(data.frame(Response = c("With other people", "Alone"),
           Frequence = as.numeric(table(d$living_arrange_1)), Percentage = round(as.numeric(prop.table(table(d$living_arrange_1)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")   

Response	Frequence	Percentage
With other people	269	85.13
Alone	47	14.87

With other people

Participants could choose multiple answers

#living_arrange  

# Create a vector with variable names
response = paste0("living_arrange_", 2:7)

# Empty vector to stor output
living_arrange_prop <- c()

# Calculate univariate proportions
for(i in response){
  living_arrange_prop[i] <- sum(d[,i]) / nrow(d)
}

# Transform
living_arrange_prop <- as.data.frame(living_arrange_prop)
living_arrange_prop$Response <- c("With a spouse (or partner)","With children","With grandchildren","With relatives or siblings?", "With friends", "With other people")

living_arrange_prop$plot<- factor(living_arrange_prop$Response, living_arrange_prop$Response)

ggplot(living_arrange_prop, aes(x = plot, y = living_arrange_prop)) + geom_bar(stat = "identity", fill = "#1596FF") + xlab("") + ylab("Percentage of participants who selected this answer") + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))

living_arrange_prop$living_arrange_prop <- round(living_arrange_prop$living_arrange_prop*100,2)

living_arrange_prop <- setcolorder(living_arrange_prop, c("Response", "living_arrange_prop"))

colnames(living_arrange_prop) <- c("Response", "Percentage of participants who selected this answer")

living_arrange_prop <- living_arrange_prop[-c(3)]
kable(living_arrange_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")  

	Response	Percentage of participants who selected this answer
living_arrange_2	With a spouse (or partner)	39.24
living_arrange_3	With children	17.72
living_arrange_4	With grandchildren	0.63
living_arrange_5	With relatives or siblings?	22.78
living_arrange_6	With friends	14.56
living_arrange_7	With other people	9.18

How many children under the age of 16 live in your household?

#children_household

ggplot(d, aes(x = d$children_household)) + geom_bar(na.rm = TRUE,fill="#1596FF", binwidth = 1) + xlab("Number of children under 16 in household")

summary(d$children_household)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.3576  0.0000  4.0000

How many adults aged 16 or older live in your household including yourself?

ggplot(d, aes(x = d$adults_household)) + geom_bar(na.rm = TRUE,fill="#1596FF", binwidth = 1) + xlab("Number of adults in household")

summary(d$children_household)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.3576  0.0000  4.0000

Were you born in Canada?

#born_can
born_can <- prop.table(table(factor(d$born_can, levels = c("1", "2"))))*100
born_can <- as.data.frame(born_can)
born_can$group <- substring(row.names(born_can), 1)
born_can$group <- revalue(as.character(born_can$group), c("1" = "Yes",  "2" = "No"))

born_can$plot <- factor(born_can$group, born_can$group)

born_can.plot <- ggplot(born_can, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) #order responses as in t5

born_can.plot + geom_histogram(aes(x = plot), data = born_can, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

## Table- 

born_can.tb <- data.frame(Response = c("Yes", "No"),
                          Frequence = as.numeric(table(d$born_can)), 
                          Percentage = round(as.numeric(prop.table(table(d$born_can)))*100,2))


kable(born_can.tb) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")   

Response	Frequence	Percentage
Yes	242	76.58
No	74	23.42

When did you move to Canada?

#move_can 

d$move_can[d$move_can==-7] <- NA
ggplot(d, aes(x = d$move_can)) + geom_histogram (na.rm=TRUE, binwidth = 1, fill="#1596FF") + xlab("Year of move to Canada")

summary(d$move_can)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1962    2005    2012    2009    2017    2018     242

To which ethnic or cultural groups did your ancestors belong? (Check all that apply)

#group id 


identity <- round(prop.table(table(factor(eth$eth, levels = c("Indigenous or Aboriginal", 
                                                              "White",
                                                              "South Asian",
                                                              "Chinese",
                                                              "Black",
                                                              "Filipino",
                                                              "Latin American",
                                                              "Arab",
                                                              "Southeast Asian",
                                                              "West Asian",
                                                              "Korean", 
                                                              "Japanese", 
                                                              "Mixed identity",
                                                              "I don't know/Prefer not to answer"))))*100,2)
identity <- as.data.frame(identity)
identity$group <- substring(row.names(identity), 1)
identity$group <- factor(identity$group, identity$group)

getPalette = colorRampPalette(brewer.pal(9, "Paired"))

identity.plot <- ggplot(identity, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 10, angle=0.45, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=getPalette(14))  +
      ylab("Percent of total") +
      xlab("")
      
identity.plot + geom_histogram(aes(x = Var1), data = identity, stat = "identity") 

#table 
identity.tb <- as.factor(eth$eth)
identity.tb <- summary(identity.tb)
identity.tb <- as.data.frame(identity.tb)
identity.tb$Var1 <- substring(row.names(identity.tb), 1)

nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$identity.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("Korean", "Japanese")
nval.df <- nval.df[-c(1)]

identity.tb <- rbind(identity.tb, nval.df)

## merge with existing prop table data used for plot above 
plot.identity.tb <- merge(identity, identity.tb, by = "Var1")
plot.identity.tb <- plot.identity.tb %>% arrange(group)
plot.identity.tb <- plot.identity.tb[-c(3)]
plot.identity.tb <- setcolorder(plot.identity.tb, c("Var1", "identity.tb", "Freq"))

colnames(plot.identity.tb) <- c("Response", "N", "Percentage")

kable(plot.identity.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Indigenous or Aboriginal	10	3.16
White	212	67.09
South Asian	17	5.38
Chinese	9	2.85
Black	8	2.53
Filipino	15	4.75
Latin American	9	2.85
Arab	3	0.95
Southeast Asian	2	0.63
West Asian	4	1.27
Korean	0	0.00
Japanese	0	0.00
Mixed identity	21	6.65
I don’t know/Prefer not to answer	6	1.90

Which category best describes your annual household income, taking into account all sources of income?

income <- prop.table(table(factor(d$income, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "77"))))*100
income <- as.data.frame(income)
income$group <- substring(row.names(income), 1)
income$group <- revalue(as.character(income$group), c("1" = "No income", "2" = "$1 to $9,999", "3" = "$10,000 to $14,999", "4" = "$15,000 to $19,999", "5" = "$20,000 to $29,999", "6" = "$30,000 to $39,999", "7" = "$40,000 to $49,999", "8" = "$50,000 to $99,999", "9" = "$100,000 to $149,999", "10" = " $150,000 to $199,999", "11" = "$200,000 or more", "12" = "Don't know/prefer no answer"))

income$plot <- factor(income$group, income$group)

income.plot <- ggplot(income, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

income.plot + geom_histogram(aes(x = plot), data = income, stat = "identity")

income.tb <- as.factor(d$income)
income.tb <- summary(income.tb)
income.tb <- as.data.frame(income.tb)
income.tb$Var1 <- substring(row.names(income.tb), 1)


income.tb$group <- revalue(as.character(income.tb$Var1), c("1" = "No income", "2" = "$1 to $9,999", "3" = "$10,000 to $14,999", "4" = "$15,000 to $19,999", "5" = "$20,000 to $29,999", "6" = "$30,000 to $39,999", "7" = "$40,000 to $49,999", "8" = "$50,000 to $99,999", "9" = "$100,000 to $149,999", "10" = " $150,000 to $199,999", "11" = "$200,000 or more", "77" = "Don't know/prefer no answer"))

## merge with existing prop table data used for plot above 
plot.income.tb <- merge(income, income.tb, by = "group")
plot.income.tb <- plot.income.tb[-c(2, 4, 6)]
plot.income.tb <- setcolorder(plot.income.tb, c("group", "income.tb", "Freq"))

plot.income.tb$order <- c(11,2,3,10,4,5,6,7,8,9,12,1)
plot.income.tb <- plot.income.tb %>% arrange(order)
plot.income.tb <- plot.income.tb[-c(4)]
colnames(plot.income.tb) <- c("Response", "N", "Percentage")


kable(plot.income.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
No income	7	2.215190
$1 to $9,999	25	7.911392
$10,000 to $14,999	24	7.594937
$15,000 to $19,999	15	4.746835
$20,000 to $29,999	24	7.594937
$200,000 or more	8	2.531646
$30,000 to $39,999	29	9.177215
$40,000 to $49,999	16	5.063291
$50,000 to $99,999	61	19.303798
$100,000 to $149,999	37	11.708861
$150,000 to $199,999	19	6.012658
Don’t know/prefer no answer	51	16.139240

To what extent does this annual household income allow you to satisfy your household’s needs?

#income_needs
income_needs <- round(prop.table(table(factor(d$income_needs, levels = c("1", "2", "3", "4", "77"))))*100,2)
income_needs <- as.data.frame(income_needs)
income_needs$group <- substring(row.names(income_needs), 1)
income_needs$group <- revalue(as.character(income_needs$group), c("1" = "Very well", "2" = "Well", "3" = "Not so well", "4" = "Not at all", "5" = "Don't know/prefer no answer"))

income_needs$group <- factor(income_needs$group, income_needs$group)

income_needs.plot <- ggplot(income_needs, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
income_needs.plot + geom_histogram(aes(x = group), data = income_needs, stat = "identity") 

## Table- 
income_needs.tb <- data.frame(Response = c("Very well", "Well", "Not so well", "Not at all", "Don't know/prefer no answer"),
           Frequence = as.numeric(table(d$income_needs)), Percentage = round(as.numeric(prop.table(table(d$income_needs)))*100,2))

kable(income_needs.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	Frequence	Percentage
Very well	70	22.15
Well	124	39.24
Not so well	86	27.22
Not at all	14	4.43
Don’t know/prefer no answer	22	6.96

What is your highest education level?

#education
education <- round(prop.table(table(factor(d$education, levels = c("1", "2", "3", "4","5", "77"))))*100,2)
education <- as.data.frame(education)
education$group <- substring(row.names(education), 1)
education$group <- revalue(as.character(education$group), c("1" = "Primary/Elementary school", "2" = "Secondary school", "3" = "Trade/Technical school or college diploma", "4" = "University degree", "5" = "Graduate degree", "6" ="I don't know/Prefer not to answer"))

education$group <- factor(education$group, education$group)

education.plot <- ggplot(education, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade)  +
      ylab("Percent of total") +
      xlab("")
      
education.plot + geom_histogram(aes(x = group), data = education, stat = "identity") 

#table 
education.tb <- as.factor(d$education)
education.tb <- summary(education.tb)
education.tb <- as.data.frame(education.tb)
education.tb$Var1 <- substring(row.names(education.tb), 1)

education.tb$group <- revalue(as.character(education.tb$Var1), c("1" = "Primary/Elementary school", "2" = "Secondary school", "3" = "Trade/Technical school or college diploma", "4" = "University degree", "5" = "Graduate degree", "6" ="I don't know/Prefer not to answer"))

## merge with existing prop table data used for plot above 
plot.education.tb <- merge(education, education.tb, by = "group")
plot.education.tb <- plot.education.tb[-c(2, 5)]
plot.education.tb <- setcolorder(plot.education.tb, c("group", "education.tb", "Freq"))

plot.education.tb$order <- c(5,1,2,3,4)
plot.education.tb <- plot.education.tb %>% arrange(order)
plot.education.tb <- plot.education.tb[-c(4)]
colnames(plot.education.tb) <- c("Response", "N", "Percentage")


kable(plot.education.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	N	Percentage
Primary/Elementary school	1	0.32
Secondary school	85	26.90
Trade/Technical school or college diploma	49	15.51
University degree	113	35.76
Graduate degree	58	18.35

What is your current employment status?

#employment
employment <- prop.table(table(factor(d$employment, levels = c("1", "2", "3", "4","5", "6"))))*100
employment <- as.data.frame(employment)
employment$group <- substring(row.names(employment), 1)
employment$group <- revalue(as.character(employment$group), c("1" = "Retired and not working", "2" = "Employed full-time", "3" = "Employed part-time", "4" = "Unemployed and looking for work", "5" = "Unemployed and not looking for work", "6" ="Other"))

employment$group <- factor(employment$group, employment$group)

employment.plot <- ggplot(employment, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteSet)  +
      ylab("Percent of total") +
      xlab("")
      
employment.plot + geom_histogram(aes(x = group), data = employment, stat = "identity") 

employment.tb <- data.frame(Response = c(  "Retired and not working",  "Employed full-time",  "Employed part-time", "Unemployed and looking for work", "Unemployed and not looking for work", "Other"),
           Frequence = as.numeric(table(d$employment)), 
           Percentage = round(as.numeric(prop.table(table(d$employment)))*100,2))

kable(employment.tb) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left") 

Response	Frequence	Percentage
Retired and not working	10	3.16
Employed full-time	121	38.29
Employed part-time	92	29.11
Unemployed and looking for work	17	5.38
Unemployed and not looking for work	20	6.33
Other	56	17.72

Section 8: Transportation by Student Status

Creating a student variable

d$student_t_f <- str_detect(d$employment_txt, "student|Student|School|school")

d <- d %>% 
    mutate(student = case_when(
        student_t_f == "TRUE" ~ "Student",
        student_t_f == "FALSE" ~ "Non Student"
))

table(d$student)

## 
## Non Student     Student 
##         271          45

Which Saskatoon Transit Go Pass do you own?

response_labels = c(
"Monthly adult pass",
"Eco Pass",
"UPass",
"Student Pass",
"Discounted Pass",
"Low Income Pass",
"I do not use a Go pass, I use a multi-use pass",
"I do not use a Go pass, I use cash",
"other"
)


ggplot(d, aes(x = factor(sask_bus_pass,
                         labels = response_labels))) + geom_bar(na.rm = TRUE, alpha = 0.65) + xlab("Pass type") + 
                          theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

On a scale of 1 to 4, with 1 being ‘very safe and 4 being ’very unsafe’, overall, how safe do you think traveling by bus is in your city?

response_labels <- c(
    "Very Safe",
    "Somewhat Safe",
    "Somewhat Unsafe",
    "Very Unsafe"
  )

ggplot(d, aes(x = factor(
  x = bus_safe,
  labels = response_labels
))) + geom_bar(na.rm = TRUE) + xlab("Safety") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

On a scale of 1 to 4, with 1 being ‘very reliable’ and 4 being ‘very unreliable’, overall, how reliable do you think traveling by bus is in your city?

response_labels <- c(
    "Very reliable",
    "Somewhat reliable",
    "Somewhat unreliable",
    "Very unreliable",
    "I don't know"
  )
ggplot(d, aes(x = factor(
  x = d$bus_reliable,
  labels = response_labels
))) + geom_bar(na.rm = TRUE) + xlab("Reliability") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

On a scale of 1 to 4, with 1 being ‘very convenient’ and 4 being very inconvenient’, overall, how convenient do you think traveling by bus is in your city?

response_labels <- c(
      "Very convenient",
      "Somewhat convenient",
      "Somewhat inconvenient",
      "Very inconvenient",
      "I don't know"
      )
ggplot(d, aes(x = factor(x = d$bus_convenient, labels = response_labels))) +
      geom_bar(na.rm = TRUE) + xlab("Convenience") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

How much more likely would you be to travel by bus more if?

a. the bus on the main part of your route ran every 10 minutes or less?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_a,
         labels = response_labels))) + 
      geom_bar(na.rm = TRUE) +
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

#### b. the bus route took you closer to your destination?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_b,
         labels = response_labels))) + 
      geom_bar(na.rm = TRUE) +
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

#### c. the bus and shelters were cleaner and in better condition?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_c,
         labels = response_labels))) +
      geom_bar(na.rm = TRUE) + 
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

#### d. the buses were on time and transfers were more reliable?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_d,
         labels = response_labels ))) + geom_bar(na.rm = TRUE) + xlab("Likelihood") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

#### e. the cost of bus passes or fare was lower?

response_labels <- c(
           "Much more likely",
           "Somewhat more likely",
           "Not at all more likely",
           "I don't know"
         )
ggplot(d,
       aes(x = factor(
         x = d$bus_moti_e,
         labels = response_labels))) +
      geom_bar(na.rm = TRUE) +
      xlab("Likelihood") +
      theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

Rank the following in order of how much they would influence your decision to use the bus.

a. the bus on the main part of your route ran every 10 minutes or less?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_a,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) +
      geom_bar(na.rm = TRUE, alpha= 0.65) +
      xlab("Rank") + 
      facet_wrap(~ student)

#### b. the bus route took you closer to your destination?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_b,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE, alpha= 0.65) + xlab("Rank") + 
                          facet_wrap(~ student)

#### c. the bus and shelters were cleaner and in better condition?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_c,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE, alpha= 0.65) + xlab("Rank") + 
                          facet_wrap(~ student)

#### d. the buses were on time and transfers were more reliable?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_d,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE, alpha= 0.65) + xlab("Rank") + 
                          facet_wrap(~ student)

#### e. the cost of bus passes or fare was lower?

response_labels <-  c(
           "1",
           "2",
           "3",
           "4",
           "5",
           "6",
           "7"
         )
rank <- factor(
         x = d$bus_moti_rank_e,
         labels = response_labels)
ggplot(d,
       aes(x = rank 
       )) + geom_bar(na.rm = TRUE , alpha= 0.65) + xlab("Rank") + 
                          facet_wrap(~ student)

Place the slider between the following features of a future bus system, based on how important they are to you, with the slider closer to the more important feature.

ggplot(d,
       aes(x = bus_moti_slider 
       )) + geom_histogram(na.rm = TRUE, bins = 15, fill= "#76D24A")  + xlab("Rank")

summary(d$bus_moti_slider) + 
                          facet_wrap(~ student)

## NULL

Do you think Saskatoon Transit Service today is:

a. Reliable

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_a,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Reliability") + 
                          facet_wrap(~ student)

b. Clean

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_b,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("cleanness") + 
                          facet_wrap(~ student)

c. Safe

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_c,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Safety") + 
                          facet_wrap(~ student)

d. Convenient

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_d,
  labels = response_labels )
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Convenience") + 
                          facet_wrap(~ student)

e. Too expensive

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_e,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Too expensive") + 
                          facet_wrap(~ student)

f. Too cheap

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all")
x <- factor(
  x = d$sask_bus_now_f,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Too cheap") + 
                          facet_wrap(~ student)

g. Professional

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_g,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Professional") + 
                          facet_wrap(~ student)

h. Environmentally friendly

response_labels <- c("Very",
             "Moderately",
             "Slightly",
             "Not at all",
             "I don't know")
x <- factor(
  x = d$sask_bus_now_h,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Environmentally friendly") + 
                          facet_wrap(~ student)

Have you ever heard of the Bus Rapid Transit (BRT) Corridors in Saskatoon?

response_labels <- c("Yes",
             "No")
x <- factor(
  x = d$brt_familiarity,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Response") + 
                          facet_wrap(~ student)

Do you think that the Bus Rapid Transit (BRT) corridors are a good or bad idea for Saskatoon? It is

response_labels <-c(
    "Very good idea",
    "Somewhat good idea",
    "Somewhat bad idea",
    "Very bad idea",
    "I don't know"
  )
x <- factor(
  x = d$brt_idea,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Response") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1)) + 
                          facet_wrap(~ student)

Will you likely travel by bus more than you currently do once the Bus Rapid Transit (BRT) corridors are in place?

response_labels <- c("Yes",
                       "No")
x <- factor(
  x = d$brt_bus_more,
  labels = response_labels)
ggplot(d,
       aes(x = x)) + geom_bar(na.rm = TRUE ,
                              alpha = 0.65) + xlab("Response") + 
                          facet_wrap(~ student)